Oracle Communications Data Model Implementation and Operations Guide, 11g Release 2 (11.2)

11g Release 2 (11.2)

E15883-04

Contents

Title and Copyright Information

Preface

- Audience

- Documentation Accessibility

- Related Oracle Resources

- Conventions

1 Introduction to Oracle Communications Data Model Customization

- What is the Oracle Communications Data Model?

- Components of the Oracle Communications Data Model

- Oracle Products That Make Up Oracle Communications Data Model

- Steps for Implementing an Oracle Communications Data Model Warehouse

- Before You Begin Customizing the Oracle Communications Data Model

- Prerequisite Knowledge for Implementors

- Responsibilities of a Data Warehouse Governance Committee

- Managing the Metadata for Oracle Communications Data Model

- Metadata Categories and Standards

- Working with a Metadata Repository

- Browsing the Metadata Repository Supplied With Oracle Communications Data Model

- Using Oracle Warehouse Builder with the Oracle Communications Data Model

- Performing Fit-Gap Analysis for Oracle Communications Data Model

2 Logical Model Customization

- Introduction to Customizing the Logical Data Model

- Data Model Notation and Modeling Tools

- Conventions When Customizing the Logical Model

- Common Change Scenarios When Modifying the Logical Model

- Examples: Modifying the Logical Model

3 Physical Model Customization

- Characteristics of the Default Physical Model

- Customizing the Physical Model

- Questions to Answer Before You Customize the Physical Model

- Conventions When Customizing the Physical Model

- General Recommendations When Designing Physical Structures

- Tablespaces in the Oracle Communications Data Model

- Data Compression in the Oracle Communications Data Model

- Types of Data Compression Available

- Example: Compressing an Oracle Communications Data Model Table

- Tables for Supertype and Subtype Entities in Oracle Communications Data Model

- Surrogate Keys in the Physical Model

- Integrity Constraints in Oracle Communications Data Model

- Indexes and Partitioned Indexes in the Oracle Communications Data Model

- Partitioned Tables in the Oracle Communications Data Model

- Partitioning the Oracle Communications Data Model for Manageability

- Partitioning the Oracle Communications Data Model for Easier Data Access

- Partitioning the Oracle Communications Data Model for Join Performance

- Parallel Execution in the Oracle Communications Data Model

- Enabling Parallel Execution for a Session

- Enabling Parallel Execution of DML Operations

- Enabling Parallel Execution at the Table Level

4 Access Layer Customization

- Introduction to Customizing the Access Layer of Oracle Communications Data Model

- Derived Tables in the Oracle Communications Data Model

- Creating New Derived Tables for Calculated Data

- Customizing Oracle Communications Data Model Data Mining Models

- Creating a New Data Mining Model for Oracle Communications Data Model

- Modifying Oracle Communications Data Model Data Mining Models

- Tutorial: Customizing the Churn Prediction Mining Model

- Aggregate Tables in the Oracle Communications Data Model

- Dimensional Components in the Oracle Communications Data Model

- Characteristics of a Dimensional Model

- Characteristics of Relational Star and Snowflake Tables

- Declaring Relational Dimension Tables

- Validating Relational Dimension Tables

- Characteristics of the OLAP Dimensional Model

- Oracle OLAP Cube Views

- Cube Materialized Views

- Characteristics of the OLAP Cubes in Oracle Communications Data Model

- Defining New Oracle OLAP Cubes for Oracle Communications Data Model

- Changing an Oracle OLAP Cube in Oracle Communications Data Model

- Creating a New Forecast Cube in Oracle Communications Data Model

- Choosing a Cube Partitioning Strategy for Oracle Communications Data Model

- Choosing a Cube Data Maintenance Method for Oracle Communications Data Model

- Materialized Views in the Oracle Communications Data Model

- Types of Materialized Views and Refresh options

- Refresh Options for Materialized Views with Aggregates

- Refresh Options for Materialized Views Containing Only Joins

- Refresh Options for Nested Materialized Views

- Choosing Indexes for Materialized Views

- Partitioning and Materialized Views

- Compressing Materialized Views

5 ETL Implementation and Customization

- The Role of ETL in the Oracle Communications Data Model

- Writing Source-ETL for Oracle Communications Data Model

- ETL Architecture for Oracle Communications Data Model Source-ETL

- Creating a Source to Target Mapping Document for the Source-ETL

- Designing a Plan for Rectifying Source-ETL Data Quality Problems

- Designing Source-ETL Workflow and Jobs Control

- Designing Source-ETL Exception Handling

- Writing Source-ETL that Loads Efficiently

- Using a Staging Area for Flat Files

- Preparing Raw Data Files for Source-ETL

- Source-ETL Data Loading Options

- Parallel Direct Path Load Source-ETL

- Partition Exchange Load for Oracle Communications Data Model Source-ETL

- Customizing Intra-ETL for the Oracle Communications Data Model

- Understanding the INTRA_ETL_FLW Process Flow

- Performing an Initial Load of an Oracle Communications Data Model Warehouse

- Executing the Default Oracle Communications Data Model Intra-ETL

- Managing Errors During Oracle Communications Data Model Intra-ETL Execution

- Monitoring the Execution of the Intra-ETL Process

- Recovering an Intra ETL Process

- Troubleshooting Intra-ETL Performance

6 Report and Query Customization

- Reporting Approaches in Oracle Communications Data Model

- Customizing Oracle Communications Data Model Sample Reports

- Writing Your Own Queries and Reports

- Optimizing Star Queries

- Troubleshooting Oracle Communications Data Model Report Performance

- Examples: As Is and As Was Reporting

- Tutorial: Creating a New Oracle Communications Data Model Dashboard

- Tutorial: Creating a New Oracle Communications Data Model Report

A Exadata and the Oracle Communications Data Model

- Benefits of Running Oracle Communications Data Model on an Exadata Storage Server

- Exadata: A Preconfigured Balanced System

- Exadata Smart Flash Cache

- Exadata Storage Capacity, Performance, Bandwidth and IOPS

- Exadata Smart Scan Processing and Storage Index

- Smart Scan Predicate Filtering

- Smart Scan Column Filtering

- Smart Scan Join Processing

- Smart Scan Processing of Encrypted Tablespaces and Columns

- Storage Indexing

- Offload of Data Mining Model Scoring

- Other Exadata Smart Scan Processing

- Other Exadata Optimization Techniques for Data Warehousing

- Suggested Practices for Oracle Communications Data Model Warehouse in Exadata

- Index Rich Versus Index Scarce Design

- Exadata Hybrid Columnar Compression

B Sizing and Configuring an Oracle Communications Data Model Warehouse

- Sizing an Oracle Communications Data Model Warehouse

- Configuring a Balanced System for Oracle Communications Data Model

- Maintaining High Throughput in an Oracle Communications Data Model Warehouse

- Configuring I/O in an Oracle Communications Data Model for Bandwidth not Capacity

- Planning for Growth of Your Oracle Communications Data Model

- Testing the I/O System Before Building the Oracle Communications Data Model Warehouse

- Balanced Hardware Configuration Guidelines for Oracle Communications Data Model

C Refreshing the Data in an Oracle Communications Data Model Warehouse

- Introduction to Refreshing the Data in an Oracle Communications Data Model Warehouse

- Refreshing Oracle Communications Data Model Relational Tables and Views

- Refreshing Oracle Communications Data Model OLAP Cubes

- Refreshing Oracle Communications Data Model Data Mining Models

D Working with User Privileges in Oracle Communications Data Model

- Accounts Created for Oracle Communications Data Model

- When You Must Consider User Privileges in an Oracle Communications Data Model

- Granting Only Select Privileges to Users of the Sample Reports

E Upgrading Oracle Communications Data Model

- Preparing to Upgrade Oracle Communications Data Model

- Upgrade Paths for Oracle Communications Data Model

- Regression Testing for Oracle Communications Data Model

Index

Oracle® Communications Data Model

Implementation and Operations Guide

11g Release 2 (11.2)

E15883-04

June 2011

Oracle Communications Data Model Implementation and Operations Guide, 11g Release 2 (11.2)

E15883-04

Copyright © 2011, Oracle and/or its affiliates. All rights reserved.

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Preface

The Oracle Communications Data Model Implementation and Operations Guide describes best practices for implementing a data warehouse based on the Oracle Communications Data Model.

This preface contains the following topics:

- Audience

- Documentation Accessibility

- Related Oracle Resources

- Conventions

Audience

This document is intended for business analysts, data modelers, data warehouse administrators, IT staff, and ETL developers who implement an Oracle Communications Data Model warehouse.

Documentation Accessibility

For information about Oracle's commitment to accessibility, visit the Oracle Accessibility Program website at http://www.oracle.com/pls/topic/lookup?ctx=acc&id=docacc.

Access to Oracle Support

Oracle customers have access to electronic support through My Oracle Support. For information, visit http://www.oracle.com/pls/topic/lookup?ctx=acc&id=info or visit http://www.oracle.com/pls/topic/lookup?ctx=acc&id=trs if you are hearing impaired.

Related Oracle Resources

Oracle provides many resources for you when implementing the Oracle Communications Data Model.

Oracle Communications Data Model Documentation Set

For more information on Oracle Communications Data Model, see the following documents in the Oracle Communications Data Model Release 11g documentation set:

- Oracle Communications Data Model Installation Guide

- Oracle Communications Data Model Reference

- Oracle Communications Data Model Release Notes

Oracle Technology Network

Visit the Oracle Technology Network (OTN to access to demos, whitepapers, Oracle By Example (OBE) tutorials, updated Oracle documentation, and other collateral.

Registering on OTN

You must register online before using OTN, Registration is free and can be done at

www.oracle.com/technetwork/index.html

Oracle Documentation on OTN

The Oracle Documentation site on OTN provides access to Oracle documentation. After you have a user name and password for OTN, you can go directly to the documentation section of the OTN Web site at

www.oracle.com/technetwork/indexes/documentation/index.html

Oracle Learning Library on OTN

The Oracle Learning Library provides free online training content (OBEs, Demos and Tutorials). After you have a user name and password for OTN, you can go directly to the Oracle Learning Library Web site at

www.oracle.com/technetwork/tutorials/index.html

Then you can search for the tutorial or demo (within "All") by name. For example, search within "All" for "OCDMTutorial" to go to the tutorial for Oracle Communications Data Model.

Conventions

The following text conventions are used in this document:

Convention

Meaning

boldface

Boldface type indicates graphical user interface elements associated with an action, or terms defined in text or the glossary.

italic

Italic type indicates book titles, emphasis, or placeholder variables for which you supply particular values.

monospace

Monospace type indicates commands within a paragraph, URLs, code in examples, text that appears on the screen, or text that you enter.

1 Introduction to Oracle Communications Data Model Customization

This chapter provides an introduction to customizing the Oracle Communications Data Model. It contains the following topics:

- What is the Oracle Communications Data Model?

- Steps for Implementing an Oracle Communications Data Model Warehouse

- Before You Begin Customizing the Oracle Communications Data Model

- Managing the Metadata for Oracle Communications Data Model

- Performing Fit-Gap Analysis for Oracle Communications Data Model

What is the Oracle Communications Data Model?

Oracle Communications Data Model is a standards-based, pre-built approach to communications data warehousing enabling a communications company to realize the power of insight more quickly. Oracle Communications Data Model reduces costs for both immediate and on-going operations by leveraging out-of-box Oracle based data warehouse and business intelligence solutions, making world-class database and business intelligence technology solutions available with a communications specific data model. You can use Oracle Communications Data Model in any application environment. Also, you can easily extend the model.

Using Oracle Communications Data Model you can jump-start the design and implementation of an Oracle Communications Data Model warehouse to quickly achieve a positive ROI for your data warehousing and business intelligence project with a predictable implementation effort.

Oracle Communications Data Model provides much of the data modeling work that you must do for a communication business intelligence solution. It comes with logical and physical data models that were designed following best practices for communications service providers. Oracle Communications Data Model is conformance certified with TM Forum's Information Framework (SID) version 8.1 as discussed in Oracle Communications Data Model Reference.

- Components of the Oracle Communications Data Model

- Oracle Products That Make Up Oracle Communications Data Model

Components of the Oracle Communications Data Model

Oracle Communications Data Model includes the following components:

- Logical model which is a third normal form (3NF) entity-object standards-based model.

- Physical model defined as two Oracle Database schemas. One schema defines all the relational and OLAP physical components of Oracle Communications Data Model, and the tables that hold the results of the data mining models. The other schema defines the actual data mining models.

- Intra-ETL database packages and SQL scripts to extract, transform, and load (ETL) data from the Oracle Communications Data Model 3NF physical tables to the derived and aggregate tables in Oracle Communications Data Model.

- Sample reports and dashboards developed using Oracle Business Intelligence Suite Enterprise Edition.

- DDL and installation scripts

Note:

When you use the Oracle Installer to install Oracle Communications Data Model, you have the choice of performing two different types of installations:

- Installation of the Oracle Communications Data Model component, itself

- Installation of sample reports (and schemas)

See Oracle Communications Data Model Installation Guide for detailed information on the different types of installation

See:

Oracle Communications Data Model Reference for detailed descriptions of the components.

Oracle Products That Make Up Oracle Communications Data Model

Several Oracle technologies are involved in building the infrastructure for Oracle Communications Data Model:

- Oracle Database with OLAP, Data Mining and Partitioning Option

- Oracle Development Tools

- Oracle Business Intelligence Suite Enterprise Edition Presentation Tools

Oracle Database with OLAP, Data Mining and Partitioning Option

Oracle Communications Data Model uses a complete Oracle technical stack. It leverages the following data warehousing features of the Oracle Database: SQL model, compression, partitioning, advanced statistical functions, materialized views, data mining, and online analytical processing (OLAP).

Oracle Development Tools

You can use the following Oracle tools to customize the predefined logical and physical models provided with Oracle Communications Data Model, or to populate the target relational tables and materialized cube views.

Table 1-1 Oracle Development Tools Used with Oracle Communications Data Model

Name

Use

Oracle SQL Data Modeler

To modify, customize, and extend the logical model

SQL Developer or SQL*Plus

To modify, customize, and extend database objects

Oracle Warehouse Builder

For the process control of the intra ETL process

Analytic Workspace Manager

To view, create, develop, and manage OLAP dimensional objects.

Oracle Business Intelligence Suite Enterprise Edition Presentation Tools

Oracle Business Intelligence Suite Enterprise Edition is a comprehensive suite of enterprise BI products that delivers a full range of analysis and reporting capabilities. You can use Oracle Business Intelligence Suite Enterprise Edition Answers and Dashboard presentation tools to customize the predefined sample dashboard reports that are provided with Oracle Communications Data Model.

See:

"Reporting Approaches in Oracle Communications Data Model".

Steps for Implementing an Oracle Communications Data Model Warehouse

Although Oracle Communications Data Model was designed following best practices for communications service providers, usually the model requires some customization to meet your business needs.

The reasons that you might customize Oracle Communications Data Model include: your business does not have a business area that is in the logical model of Oracle Communications Data Model, or you must apply a new or different business rule. Typical physical model modifications include: adding, deleting, modifying, or renaming tables and columns; or altering foreign keys, constraints, or indexes.

To implement an Oracle Communications Data Model warehouse, take the following steps:

1. Perform the organizational tasks outlined in "Before You Begin Customizing the Oracle Communications Data Model".

2. Create a fit-gap analysis report by following the process outlined "Performing Fit-Gap Analysis for Oracle Communications Data Model".

3. In a development environment, install a copy of the Oracle Communications Data Model.

4. Customize Oracle Communications Data Model by making the changes you documented in the fit-gap analysis report. Make the changes in the following order:

1. Logical model. Follow the guidelines in Chapter 2, "Logical Model Customization."

2. Foundation layer of the physical model and the ETL to populate that layer. When designing the physical objects, follow the guidelines in Chapter 3, "Physical Model Customization." When writing the ETL, follow the guidelines in "Writing Source-ETL for Oracle Communications Data Model".

3. Access layer of the physical model. When designing the physical objects, follow the guidelines in Chapter 4, "Access Layer Customization." When writing the ETL, follow the guidelines in "Customizing Intra-ETL for the Oracle Communications Data Model".

5. In a test environment, make a copy of your customized version of Oracle Communications Data Model. Then, following the documentation you created in Step 2, test the customized version of Oracle Communications Data Model

6. Following your typical procedures, roll the tested customized version of Oracle Communications Data Model out into pre-production and, then, production.

IMPORTANT:

Keep 'clean' copies of the components delivered with Oracle Communications Data Model components. This is important when upgrading to later versions of Oracle Communications Data Model.

Before You Begin Customizing the Oracle Communications Data Model

Before you begin customizing Oracle Communications Data Model, ensure the following teams and committees exist:

- Data warehouse governance steering committee. This steering committee has the responsibilities outlined in "Responsibilities of a Data Warehouse Governance Committee".

- Implementation team. This team consists of IT engineers who have the expertise outlined in "Prerequisite Knowledge for Implementors". This team has the responsibilities outlined in "Steps for Implementing an Oracle Communications Data Model Warehouse".

- Fit-gap analysis team. This team consists of business analysts who can identify the business requirements and scope of the Oracle Communications Data Model and at least some engineers in the Implementation team. Business members of this team must understand logical data modeling so that they can evaluate what changes must be made to the logical model and the access layer of the physical model. This team has the responsibilities outlined in "Performing Fit-Gap Analysis for Oracle Communications Data Model".

After these teams and committees are formed:

- Discuss the approach and determine the involvement and roles of every party involved in the customization (for example, business and IT).

- Agree on the scope of the project (that is, agree on what new data must be in the data warehouse and why it is needed).

- Agree on the timing and the working arrangements.

Prerequisite Knowledge for Implementors

As outlined in "Oracle Products That Make Up Oracle Communications Data Model", the Oracle Communications Data Model uses much of the Oracle stack. Consequently, to successfully implement the Oracle Communications Data Model, the implementation team needs:

- Experience performing information and data analysis and data modeling. (Experience using Oracle SQL Data Modeler, is a plus.)

- An understanding of the Oracle technology stack, especially data warehouse (Database, Data Warehouse, OLAP, Data Mining, Warehouse Builder, Oracle Business Intelligence Suite Enterprise Edition)

- Hands-on experience using:

- Oracle Database

- PL/SQL

- SQL DDL and DML syntax

- Analytic Workspace Manager

- Oracle SQL Developer

- Oracle Business Intelligence Suite Enterprise Edition Administrator, Answers, and Dashboards

Responsibilities of a Data Warehouse Governance Committee

Governance is of concern to any enterprise, executive team or individual with an interest in the processes, standards, and compliance. It is even more important to organizations that have invested in data warehousing.

Data warehouse governance occurs within the context of overall IT governance. It provides the necessary policies, process and procedures, which must be clearly communicated to the entire corporation, from the IT employees to the front-end operational personnel.

Before you customize Oracle Communications Data Model, setup a data warehouse governance steering committee if one does not exist. The role of this steering committed is to oversee the data warehouse to provide an environment that reaches across the enterprise and drives the best business value.

Data Warehouse Governance Committee: Overall Responsibilities

The data warehouse governance steering committee sets direction and response for the governance framework and covers the follow areas:

- The entire data warehouse life cycle.

- Agree on the data to process and make available to end-users.

- Determine what is the minimum quality criteria for the data that is available to end users and determine how to measure and analyze these criteria against the quality of the data that is the source data for the data warehouse.

- The business goals of the organization to apply core information from data warehouse.

- The policies, procedures and standards for data resource and data access.

- The life cycle of data warehouse component management.

Data Warehouse Governance Committee: Data Governance Responsibilities

The more detailed focus in data warehouse governance is data governance. Data governance tasks include:

- Approving the data modeling standards, metadata standards and other related standards. This includes determining a metadata strategy as discussed in "Managing the Metadata for Oracle Communications Data Model"' and identifying the data modeling tools to use that support these standards.

- Determining the data retention policy.

- Designing a data access policy based on legal restrictions and data security rules.

- Designing a data backup strategy that aligns with the impact analysis to the business unit.

- Monitoring and reporting on data usage, activity, and alerts.

Managing the Metadata for Oracle Communications Data Model

Metadata is any data about data and, as such, is an important aspect of the data warehouse environment. Metadata allows the end user and the business analyst to navigate through the possibilities without knowing the context of the data or what the data represents.

Metadata management is a comprehensive, ongoing process of overseeing and actively managing metadata in a central environment which helps an enterprise to identify how data is constructed, what data exists, and what the data means. It is particularly helpful to have good metadata management when customizing Oracle Communications Data Model so that you can do impact analysis to ensure that changes do not adversely impact data integrity anywhere in your data warehouse.

- Metadata Categories and Standards

- Working with a Metadata Repository

- Browsing the Metadata Repository Supplied With Oracle Communications Data Model

- Using Oracle Warehouse Builder with the Oracle Communications Data Model

Metadata Categories and Standards

Metadata is organized into three major categories:

- Business metadata describes the meaning of data in a business sense. The business interpretation of data elements in the data warehouse is based on the actual table and column names in the database. Gather this mapping information and business definition and rules information into business metadata.

- Technical metadata represents the technical aspects of data, including attributes such as data types, lengths, lineage, results from data profiling, and so on

- Process execution metadata presents statistics on the results of running the ETL process itself, including measures such as rows loaded successfully, rows rejected, amount of time to load, and so on

Since metadata is so important in information management, many organizations attempt to standardize metadata at various levels, such as:

- Metadata Encoding and Transmission Standard (METS). A standard for encoding descriptive, administrative, and structural metadata regarding objects within a digital library.

- American National Standards Institute (ANSI). The organization that coordinates the U.S. voluntary standardization and conformity-assessment systems.

- International Organization for Standardization (ISO). The body that establishes, develops, and promotes standards for international exchange.

- Common Warehouse Metamodel (CWM). A specification, released and owned by the Object Management Group, for modeling metadata for relational, non-relational, multi-dimensional, and most other objects found in a data warehousing environment.

When you implement your metadata management solution, reference your data warehouse infrastructure environment and make the decision which standard to follow.

Working with a Metadata Repository

You manage metadata using a Metadata Repository. At the highest level, a Metadata Repository includes three layers of information. The layers are defined in the following order:

1. A Physical layer. This metadata layer identifies the source data.

2. A Business Model and Mapping layer. This metadata layer organizes the physical layer into logical categories and records the appropriate metadata for access to the source data.

3. The Presentation layer. This metadata layer exposes the business model entities for end-user access.

The first step in creating a Metadata Repository is to scope your metadata management needs by:

- Identifying the metadata consumers. Typically, there are business consumers and technical consumers.

- Determine the business and technical metadata requirements.

- Aligning metadata requirements to specific data elements and logical data flows.

Then:

- Decide how important each part is.

- Assign responsibility to someone for each piece.

- Decide what constitutes a consistent and working set of metadata

- Where to store, backup, and recover the metadata.

- Ensure that each piece of metadata is available only to those people who need it.

- Quality-assure the metadata and ensure that it is complete and up to date.

- Identify the Metadata Repository to use and how to control that repository from one place

After creating the metadata definitions, review your data architecture to ensure you can acquire, integrate, and maintain the metadata.

As the data keeps on changing in your data warehouse day by day, update the Metadata Repository. When you want to change business rules, definitions, formulas or process (especially when customizing the Oracle Communications Data Model), your first step is to survey the metadata and do an impact analysis to list all of the attributes in the data warehouse environment that would be affected by a proposed change.

Browsing the Metadata Repository Supplied With Oracle Communications Data Model

To customize the Oracle Communications Data Model model, you must understand the dependencies among Oracle Communications Data Model components -- especially how the report KPIs are mapped to the physical tables and columns.

Oracle Communications Data Model provides a tool called the "OCDM Metadata" browser that helps you discover these dependencies. When you install Oracle Communications Data Model with its sample reports, the metadata browser is delivered as a sample Dashboard.

See:

Oracle Communications Data Model Installation Guide for more information on installing the sample reports and deploying the Oracle Communications Data Model RPD and webcat on the Business Intelligence Suite Enterprise Edition instance.

There are four tabs in the Oracle Communications Data Model Metadata browser:

- Measure-Entity tab

- Entity-Measure tab

- Program-Table tab

- Table-Program tab

Measure-Entity tab

On the Measure-Entity tab you can see the business areas (relational, OLAP, mining), the measures description, corresponding formula, responsible entities, and attributes for the measure.

To browse the data, select the business area and measure description that you are interested in.

Entity-Measure tab

Using the Entity-Measure tab of the Metadata Browser, you can discover the mappings between entities, attributes, supported measures, and calculations of the measures. You can discover information about particular entities and attributes.

For example, you take the following steps to learn more about the COST ORIGINAL MONTH AGGR entity:

1. Select the COST ORIGINAL MONTH AGGR entity.

2. Click GO.

Program-Table tab

Using the Program-Table tab you can browse for information on the intra-ETL mappings and report information. Take the following steps:

1. Select the program type (that is, intra-ETL or report) and program name for showing particular report or intra-ETL information.

2. Select GO.

Table-Program tab

By default when you go to the Table-Program tab you see all of the tables used for all the reports.

To discover what reports use a particular table, you must move a particular table from the right pane to the left (Selected) pane.

For example, to see the reports that use the DWA_CALL_CENTR_CALL_MO table, take the following steps:

1. In the right pane of the Table-Program tab, select DWA_CALL_CENTR_CALL_MO.

2. Move it to the Selected list on the left by clicking on < (left arrow), and click OK.

3. Select GO.
The reports for DWA_CALL_CENTR_CALL_MO table are displayed.

Using Oracle Warehouse Builder with the Oracle Communications Data Model

Oracle Warehouse Builder provides excellent metadata management features that you can use with Oracle Communications Data Model.

Before you import source metadata into Oracle Warehouse Builder, you create a module to contain metadata definitions. The type of module you create depends on the source from which you are importing metadata into your Metadata Repository. Oracle Warehouse Builder supports many different sources and targets as discussed in Oracle Warehouse Builder Sources and Targets Guide. For example, when connecting to an Oracle database, Oracle Warehouse Builder queries the database dictionary to extract all needed metadata on tables, views, sequences, dimensions, cubes, data types, PL/SQL packages, and so on.

Oracle Warehouse Builder also provides an interface tool called the Metadata Dependency Manager through which you can explore dependencies among data objects, as represented by the metadata in your Oracle Warehouse Builder repository. The Metadata Dependency Manager presents dependencies in the form of interactive lineage and impact diagrams. A lineage diagram traces the data flows for an object back to the sources and displays all objects along those paths. An impact diagram identifies all the objects that are derived from the selected object.

Information provided by the Metadata Dependency Manager can help you in many circumstances. For example:

1. Starting from a target object, such as a dimension, cube, or business intelligence tool report, you can identify the columns in each data source that are used in computing the results in the target.

2. You can assess the impact of design changes in an object such as a source table or a pluggable mapping that is used throughout a project.

3. You can propagate a design change, such as a change to the data type of a source table column, to downstream objects in the design.

Using end-to-end data lineage and impact analysis reduces project risk by allowing better planning for design changes, faster identification of unanticipated impacts when source systems change, and enabling more effective auditing of your business intelligence results, master data or other data integration processes.

You can also define and use SQL- or XML-based custom metadata stores to retrieve definitions of source and target objects such as tables and views.

For data files extracted from some mainframe sources, Oracle Warehouse Builder can interpret Cobol Copybook files that describes the structure of the data file, and create its source metadata based on that.

Oracle Warehouse Builder application adapters or application connectors provide additional metadata about ERP and CRM application sources.

Oracle Warehouse Builder can deploy or execute process flows and schedules to Oracle Enterprise Manager and Oracle Workflow. In general, you can deploy a schedule in any Oracle Database location, release 10g or later.

Performing Fit-Gap Analysis for Oracle Communications Data Model

Fit-gap analysis is where you compare your information needs and communication business requirements with the structure that is available with Oracle Communications Data Model. You identify any required functionality that is not included in the logical model and the default schema, as well as other modifications that are necessary to meet your requirements.

The result of your fit-gap analysis is a customization report which is a brief explanation of the adaptations and adjustments required to customize Oracle Communications Data Model to fit your communication environment.

The fit-gap analysis team writes the customization report by taking the following steps:

1. If you have performed previous evaluations, review the documentation from the previous phases, and if necessary add team members with the required business and technical expertise.

2. Review the data and map your logical entities and data structure with the Oracle Communications Data Model logical model and schema:

- Starting from business requirements, questions, and rules, identify any entities and attributes that are not in the Oracle Communications Data Model.

- Compare the Oracle Communications Data Model to your existing application model if have one.

- Compare the Oracle Communications Data Model to the OLTP data that you are using as a data source to the Oracle Communications Data Model warehouse.

3. Determine the differences between your needs and Oracle Communications Data Model schema. To help you with this task, produce a list of actions people may take with the system (examples rather than models), and create use cases for appraising the functionality of the Oracle Communications Data Model Warehouse. Answer the following questions about the differences you find:

- Which differences you can live with, and which must be reconciled?

- What can you do about the differences you cannot live with?

4. Identify the changes you must make to the default design of Oracle Communications Data Model to create the customized warehouse. Identify these changes in the following order:

1. Logical model. Follow the guidelines outlined in Chapter 2, "Logical Model Customization".

2. Logical to physical mapping. Follow the guidelines outlined in Chapter 5, "ETL Implementation and Customization" to identify and design the source-ETL that you must write and any changes you must make to the intra-ETL provided with Oracle Communications Data Model.

3. Physical model. Follow the guidelines outlined in Chapter 3, "Physical Model Customization".

Tip:

When identifying changes, ensure that the changes meet your security and metadata requirements.

5. Write the customization report, detailing what changes are required to make the Oracle Communications Data Model match your business needs. This includes any additions and changes to interfaces to existing systems.

6. Based on the customization report, update the Project Plan and perform the steps outlined in "Steps for Implementing an Oracle Communications Data Model Warehouse".

2 Logical Model Customization

This chapter provides information about customizing the logical model portion of the Oracle Communications Data Model. It contains the following topics:

- Introduction to Customizing the Logical Data Model

- Common Change Scenarios When Modifying the Logical Model

- Examples: Modifying the Logical Model

Introduction to Customizing the Logical Data Model

Tip:

If you do not have a requirement to change the logical design delivered with Oracle Communications Data Model you can ignore this topic

A logical data model is the model that is built during the strategy and analysis phase of the Database Development life cycle. The purpose of this model is to clarify and satisfy the needs of the business users and validate the information requirements. Typically, a logical model is in Third Normal Form (3NF) which is a classical relational-database modeling technique that minimizes data redundancy through normalization.

The logical model delivered with Oracle Communications Data Model is a Third Normal Form (3NF) model developed using Oracle SQL Developer Data Modeler.

When customizing the logical data model of Oracle Communications Data Model, keep the data model application-neutral, and ideally in Third Normal Form. That way a physical design based on it mirrors the logical data model structure, and realizes all the benefits of a good relational database design: flexibility, controlled and limited redundancy, and ease of future expansion and support.

Approach any attempt to change the Oracle Communications Data Model with caution. The logical model of the Oracle Communications Data Model has (at its core) a set of generic structures that allow it to be flexible and extensible. Before making extensive additions, deletions, or changes, ensure that you understand the full range of capabilities of Oracle Communications Data Model and that you cannot handle your requirements using the default logical objects.

- Conventions When Customizing the Logical Model

- Common Change Scenarios When Modifying the Logical Model

- Examples: Modifying the Logical Model

Data Model Notation and Modeling Tools

When designing the default Oracle Communications Data Model the following notation and tools were used. Determine if you want to customize Oracle Communications Data Model in the same manner:

Data Model Notation

Data model uses the Information Engineering (IE) syntax.

Data Modeling Tools

All of the data models are developed and maintained using Oracle SQL Developer Data Modeler.

Conventions When Customizing the Logical Model

When developing the logical model for Oracle Communications Data Model, the following conventions were followed. Continue to follow these conventions as you customize the logical model.

Naming Conventions for the Logical Data Model

In the logical data model, ensure that entity and attribute names are meaningful in your business. Also, to ensure that when you create the physical data model you can use physical table names that are similar to the logical entity names. Also, familiarize yourself with the naming restrictions within an Oracle Database schema.

For example:

- Table and column names:

- Must start with a letter

- Can use only 30 alphanumeric characters or less

- Cannot contain spaces or some special characters such as "!".

- Cannot use reserved words

- Table names must be unique within a schema that is shared with views and synonyms.

- Column names must be unique within a table

Domain Definition Standards

A domain is a set of values allowed for a column. The domain can be enforced by a foreign key, check constraints, or the application on top of the database. Define the standards for each domain across the model such as:

- Date and time type, such as 'YYYY-MM-DD'.

- Numeric value in different situations.

- Character string length in different situations.

- Coded value definition such as key or description.

Logical Data Model Comments

Comment your logical data model using the following guidelines:

- All entities must have description (or notes).

- All attributes must have comment (in short). The description (or notes) is required to further define the attributes.

- Comments for relationships are not mandatory. But for tricky or abstruse relationships, add comments whenever you find them confusing.

Common Change Scenarios When Modifying the Logical Model

There are several common change scenarios when customizing the logical model:

- Additions to Existing Structures
If you identify business areas or processes that are not supported in the default logical model of the Oracle Communications Data Model, add new entities and attributes.

Carefully study the logical data model to avoid building redundant structures when making additions. If these additions add high value to your business value, communicate the additions back to the Oracle Communications Data Model Development Team for possible inclusion in future releases of Oracle Communications Data Model.

- Changes to Existing Structures
In some situations some structures in the logical model of the Oracle Communications Data Model may not exactly match the corresponding structures that you use.Before implementing changes, identify the impact that the changes would have on the database design of Oracle Communications Data Model. Also identify the impact on any applications based on the new design.

- Deletions of Existing Structures
If there are areas of the model that cannot be matched to any of the business requirements of your legacy systems, it is safer to keep these structures and not populate that part of the warehouse.

Deleting an entity in a logical data model can destroy relationships needed in other parts of the model or by applications based on the it. Some entities may not be needed during the initial implementation, but the you may want to use these structures at a later time. If this is a possibility, keeping the structures now saves re-work later. If entities are deleted, perform a thorough analysis to identify all relationships originating from that entity.

Examples: Modifying the Logical Model

Application stores are the latest battleground for mobile phone companies seeking to differentiate their products in an increasingly crowded and competitive marketplace. Users can find the applications by searching using keywords or by browsing through categories and top downloads - so they can see what everyone else is downloading - in addition to the front page, which showcases a selection of featured applications. The 'App World' can be accessed directly through the handset, enabling users to download applications and software on the go. Some applications are free, others cost a small fee.

Now, as an example, let's take a look how Oracle Communications Data Model supports the application store business, what a sample customer might discover during fit-gap analysis, and how that customer might extend Oracle Communications Data Model to fit the discovered gaps. (For other examples, see business use cases in Oracle Communications Data Model Reference.)

The entities provided with the logical model of Oracle Communications Data Model that support the Application stores are:

- CONTENT PROVIDER: This entity provides the actual content that ultimately is consumed by the end user. The contents could be video or audio clips or text content. In the case of Quick Time 7, the content provider is Apple.

- CONTENT TYPE: This entity stores the type of content. For example, for Quick Time 7, Content Type is an application.

- CONTENT: This entity keeps all contents provided by the content provider. For example, Quick Time 7 is Content. In this entity you can store the information about the content; for example, whether the content is still active or not, popularity of the content which is calculated by the number of downloads, and so on.

- CONTENT PRICE: This entity contains the price for downloading and ordering the content. This price is for individual content clip. There might be other contents priced as a flat rate rather than different price for each content. In this case, the pricing information is in product rating plan. For example, Quick Time 7 Pro is $30

- CONTENT PRICING TYPE: This entity is type of content pricing. For example, The charge per downloading, monthly fixed rate, and so on.

- CONTENT DELIVERY EVENT: This entity is an event to track downloads of contents. This entity captures the details of the download like the size of the download, duration, date and so on.

Assume that during the fit-gap analysis, you discover the following needs that are not supported by the logical model delivered with Oracle Communications Data Model:

- Your company saves more than two levels of data for content. In other words, you discover that you track not only Content and Content Type as represented in the standard logical model of Oracle Communications Data Model, but also a level under Content Type (that is, a content subtype). For example for the value of Games in Content, you have Content Types of Fun Games, Action, and Racing. For the value of Fun Games in Content Types, you have subtype values of Packman, Mummy, and NFS.

- In the Application Store, the operator must maintain a history of Customers who purchased applications so that when a customer tries to reinstall an application, they are not charged again.

To support the differences, you must extend the logical model in the following ways:

- To store details about type of the game you must modify the logical model. The classic way to store details is to add another entity to hold another level of information. For example, you can add an entity named CONTENT SUBTYPE that has a one-to-many relationship with the CONTENT entity. The specific steps to perform these actions in Oracle SQL Developer Data Modeler are given in Example 2-1, "Extending the Logical Model to Support Multiple Levels for CONTENT".

- To maintain the history of Customers who have purchased an application, you can change the logical model to support this need by creating an entity named APPLICATION SUBSCRIPTION that has a one-to-many relationship with CONTENT. The specific steps to perform these actions in Oracle SQL Developer Data Modeler are given in Example 2-2, "Extending the Logical Model to Maintain a History of Purchasers of an Application".

Example 2-1 Extending the Logical Model to Support Multiple Levels for CONTENT

Take the following steps in Oracle SQL Developer Data Modeler to extend the logical model to support multiple levels for CONTENT:

1. Open the Oracle Communications Data Model in SQL Developer Data Modeler.

2. Create the CONTENT SUBTYPE entity by taking the following steps:

1. In the right-hand pane (the main area) of the SQL Developer Data Modeler window, select the Logical tab.

2. Click the New Entity icon.

3. Click in the logical model pane and draw an entity box.
The Entity Properties window is displayed.

4. In the left-hand pane of Entity Properties, select General. For Name, specify, CONTENT SUBTYPE. (Note the value of Entity_xxx where xxx is the number for Long Name and Single Table for FWD Engineer Strategy.)

5. In the left-hand pane, select Attributes. Using the Add (+) icon, add the following attributes, one at a time:
CONTENT SUBTYPE CODE with a data type of CODE.

CONTENT SUBTYPE NAME with a data type of NAME.

CONTENT SUBTYPE DESC with a data type of DESCRIPTION.

6. In the left-hand pane, select Unique Identifiers. Using the Add (+) icon, add a unique, primary key to CONTENT SUBTYPE.

7. Select key_1 (which represents the unique, primary key), and use Properties icon to add the properties to the key.

8. In the left-hand pane of Key Properties, select General. For key properties, specify CST_PK for Name and Primary Key for State. (Note that the Long Name is CONTENT_SUBTYPE.CST_PK.

9. Select Attributes and Relations, and then select the column for the primary key (that is, CONTENT SUBTYPE CODE).

10. Click Apply. The entity CONTENT SUBTYPE is created.

3. Add a CONTENT SUBTYPE CODE column to the CONTENT entity by taking the following steps:

1. Select the CONTENT entity.

2. Right click and select Properties.

3. In the left-hand pane, click Attributes.

4. Use the Add (+) icon to add an attribute with the Name of CONTENT SUBTYPE CODE and a Data type of CODE.

4. Add a relationship between CONTENT and CONTENT SUBTYPE by taking the following steps:

1. Select the New 1: N Relation icon.

2. Select the CONTENT SUBTYPE entity.

3. Drag the CONTENT SUBTYPE entity over to the CONTENT entity and drop the cursor.

Example 2-2 Extending the Logical Model to Maintain a History of Purchasers of an Application

Take the following steps in Oracle SQL Developer Data Modeler to extend the logical model to support maintaining a history of Customers who purchased which application:

1. Open Oracle Communications Data Model in SQL Developer Data Modeler.

2. Create an APPLICATION SUBSCRIPTION entity by taking the following steps:

1. In the right-hand pane (main area) of the SQL Developer Data Modeler window, select the Logical tab.

2. Click the New Entity icon.

3. Click in the logical model pane in the main area; and in the Logical pane press, diagonally drag, and release the mouse button to draw an entity box.
The Entity Properties window is displayed.

4. In the left-hand pane of Entity Properties, select General. Specify APPLICATION SUBSCRIPTION for Name, and SUBSCRIPTION for Super Type. (Note that the value of FWD Engineer Strategy is Single Table.)

5. In the left-hand pane, select Attributes. Using the Add (+) icon, add the attribute named APPLICATION SUBSCRIPTION CODE with a data type of CODE. (As the APPLICATION SUBSCRIPTION entity is of SUBSCRIPTION, attributes are inherited from SUBSCRIPTION entity.)

6. In the left-hand pane, select Unique Identifiers. Use the Add (+) icon to add the Primary Key

7. Select Attributes and Relations, and then select the column for the unique, primary key.

8. Click Apply. The APPLICATION SUBSCRIPTION entity is created.

3. Add APPLICATION SUBSCRIPTION CODE to CONTENT by taking the following steps:

1. Select the CONTENT entity. Right click the CONTENT entity and select Properties.

2. In the left-hand pane, select Attributes. Use the Add (+) icon to add attributes, one at a time.

4. Add a relationship to CONTENT with APPLICATION SUBSCRIPTION by taking the following steps:

1. Select the New 1: N Relation icon.

2. Select the APPLICATION SUBSCRIPTION entity.

3. Drag the APPLICATION SUBSCRIPTION entity over to the CONTENT entity and drop the cursor.

3 Physical Model Customization

This chapter provides general information about customizing the physical model of Oracle Communications Data Model and more detailed information about customizing the foundation layer of the physical model. This chapter contains the following topics:

- Characteristics of the Default Physical Model

- Customizing the Physical Model

- General Recommendations When Designing Physical Structures

See also:

Chapter 4, "Access Layer Customization"

Characteristics of the Default Physical Model

The default physical of Oracle Communications Data Model defines:

1300+ tables and 11,000+ columns

1380+ industry-specific measures and KPIs

6 pre-built data mining models

20 pre-built OLAP cubes

The default physical model of the Oracle Communications Data Model shares characteristics of a multischema "traditional" data warehouse, as described in "Layers in a "Traditional" Data Warehouse", but defines all data structures in a single schema as described in "Layers in the Default Oracle Communications Data Model Warehouse".

Layers in a "Traditional" Data Warehouse

Historically, three layers are defined for a data warehouse environment:

- Staging layer. This layer is used when moving data from the OLTP system and other data sources into the data warehouse itself. It consists of temporary loading structures and rejected data. Having a staging layer enables the speedy extraction, transformation and loading (ETL) of data from your operational systems into data warehouse without disturbing any of the business users. It is in this layer the much of the complex data transformation and data quality processing occurs. The most basic approach for the design of the staging layer is as a schema identical to the one that exists in the source operational system.

Note:

In some implementations this layer is not necessary, because all data transformation processing is done "on the fly" as data is extracted from the source system before it is inserted directly into the foundation layer.

- Foundation or integration layer. This layer is traditionally implemented as a Third Normal Form (3NF) schema. A 3NF schema is a neutral schema design independent of any application, and typically has a large number of tables. It preserves a detailed record of each transaction without any data redundancy and allows for rich encoding of attributes and all relationships between data elements. Users typically require a solid understanding of the data to navigate the more elaborate structure reliably. In this layer data begins to take shape and it is not uncommon to have some end-user application access data from this layer especially if they are time sensitive, as data becomes available here before it is transformed into the Access and Performance layer.

- Access layer. This layer is traditionally defined as a snowflake or star schema that describes a "flattened" or dimensional view of the data.

Layers in the Default Oracle Communications Data Model Warehouse

Oracle Communications Data Model warehouse environment also consists of three layers However, as indicated by the dotted line in Figure 3-1, "Layers of an Oracle Communications Data Model Warehouse", in the Oracle Communications Data Model the definitions of the foundation and access layers are combined in a single schema.

Figure 3-1 Layers of an Oracle Communications Data Model Warehouse

The layers in the Oracle Communications Data Model warehouse are:

- Staging layer. As in a "traditional" data warehouse environment, an Oracle Communications Data Model warehouse environment can have a staging layer. Because the definition of this layer varies by customer, a definition of this area is not provided as part of Oracle Communications Data Model.

- Foundation and Access layers. The physical objects for these layers are defined in a single schema, the ocdm_sys schema:

- Foundation layer. The foundation layer of the Oracle Communications Data Model is defined by base tables that present the data in 3NF (that is, tables that have the DWB_ prefix). This layer also includes reference, lookup, and control tables defined in the ocdm_sys schema (that is, the tables that have the DWR_ , DWL_, DWC_ prefixes).

- Access layer. The Access layer of Oracle Communications Data Model is defined by derived and aggregate tables (defined with DWD_ and DWA_ prefixes), cubes (defined with a CB$ prefix), and views (that is, views defined with the _VIEW suffix). These structures provide a summarized or "flattened" perspectives of the data in the foundation layer.
This layer also contains the results of the data mining models which are stored in derived (DWD_) tables. The models themselves are defined in the ocdm_mining schema.

See:

Oracle Communications Data Model Reference for detailed information on the ocdm_sys schema.

Customizing the Physical Model

The starting point for a physical data model is the 3NF logical data model. A physical data model mirrors the logical model as much as possible, (although some changes in the structure of the tables or columns may be necessary) and defines database objects (such as tables, cubes, views).

To customize the default physical model of the Oracle Communications Data Model take the following steps:

1. Answers the questions outlined in "Questions to Answer Before You Customize the Physical Model".

2. Familiarize yourself with the characteristics of the physical model as outlined in"Characteristics of the Default Physical Model".

3. Modify the foundation level of your physical model of Oracle Communications Data Model so that it mirrors your customized logical model.
When defining physical structures:

- Keep the foundation in 3NF form.

- Use the information presented in "General Recommendations When Designing Physical Structures" to guide you when designing the physical objects.

- Follow the conventions used when creating the default physical model of Oracle Communications Data Model as outlined in "Conventions When Customizing the Physical Model".

Tip:

Package the changes you make to the physical data model as a patch to the ocdm_sys schema.

4. Modify the access layer of your physical model of Oracle Communications Data Model as discussed in Chapter 4, "Access Layer Customization".

Questions to Answer Before You Customize the Physical Model

When designing the physical model, remember that the logical data model is not one-to-one with the physical data model. Consider the load, query, and maintenance requirements when you convert the logical data model into the physical layer. For example, answer the following questions before you design the physical data model:

- Do you need the physical data model to cover the full scope of the logical data model, or only part of the scope?

- What is the result of the source data profile?

- What is the data load frequency for each table?

- How many large tables are there and which tables are these?

- How will the tables and columns be accessed? What are the common joins?

- What is your data backup strategy?

Conventions When Customizing the Physical Model

When developing the physical model for Oracle Communications Data Model, the conventions outlined below were followed. Continue to follow these conventions as you customize the physical model.

General Naming Conventions for Physical Objects

Follow these guidelines for naming physical objects that you define:

- When naming the physical objects follow the naming guidelines for naming objects within an Oracle Database schema.

- Although it is common to use abbreviations in the physical modeling stage, as much as possible, use names for the physical objects that correspond to the names of the entities in the logical model. Use consistent abbreviations to avoid programmer and user confusion.

- When naming columns, use short names if possible. Short column names reduce the time required for SQL command parsing.

- The ocdm_sys schema delivered with Oracle Communications Data Model uses the prefixes and suffixes shown in the following table to identify object types.

Prefix or Suffix

Used for Name of These Objects

CB$

Materialized view of an OLAP cube. This materialized view is automatically created by the OLAP server.
Note: Do not report or query against this object. Instead access the corresponding _VIEW object.

DMV_

Materialized view created for performance reasons (that is, not an aggregate table or an OLAP cube).

DWA_

Aggregate tables which are materialized views.

DWB_

Base transaction data (3NF) tables.

DWC_

Control tables.

DWD_

Derived tables -- including data mining result tables.

DWL_

Lookup tables.

DWV_

Relational view of time dimension

DWR_

Reference data tables.

_VIEW

A relational view of an OLAP cube, dimension, or hierarchy.

Use these prefixes and suffixes for any new tables, views, and cubes that you define.

See:

Oracle Communications Data Model Reference for detailed information about the objects in the default Oracle Communications Data Model.

Domain definition

Continue with the domain standards that you followed during the logical modeling phase as described in"Domain Definition Standards".

General Recommendations When Designing Physical Structures

The ocdm_sys schema delivered with Oracle Communications Data Model was designed and defined following best practices for data access and performance. Continue to use these practices when you add new physical objects. This section provides information about how decisions about the following physical design aspects were made to the default Oracle Communications Data Model:

- Tablespaces in the Oracle Communications Data Model

- Data Compression in the Oracle Communications Data Model

- Tables for Supertype and Subtype Entities in Oracle Communications Data Model

- Surrogate Keys in the Physical Model

- Integrity Constraints in Oracle Communications Data Model

- Indexes and Partitioned Indexes in the Oracle Communications Data Model

- Partitioned Tables in the Oracle Communications Data Model

- Parallel Execution in the Oracle Communications Data Model

Tablespaces in the Oracle Communications Data Model

A tablespace consists of one or more data files, which are physical structures within the operating system you are using.

Recommendations

If possible, define tablespaces so that they represent logical business units.

Use ultra large data files for a significant improvement in very large Oracle Communications Data Model warehouse.

Changing the Tablespace and Partitions Used by Tables

You can change the tablespace and partitions used by Oracle Communications Data Model tables. What you do depends on whether the Oracle Communications Data Model table has partitions:

- For tables that do not have partitions (that is, lookup tables and reference tables), you can change the existing tablespace for a table.
By default, Oracle Communications Data Model defines the partitioned tables as interval partitioning, which means the partitions are created only when new data arrives.

Consequently, for Oracle Communications Data Model tables that have partitions (that is, Base, Derived, and Aggregate tables), for the new interval partitions to be generated in new tablespaces rather than current ones, issue the following statements.

ALTER TABLE table_name MODIFY DEFAULT ATTRIBUTES
TABLESPACE new_tablespace_name;

When new data is inserted in the table specified by table_name, a new partition is automatically created in the tablespace specified by tablespace new_tablespace_name.

- For tables that have partitions (that is, base, derived, and aggregate tables), you can specify that new interval partitions be generated into new tablespaces.
For Oracle Communications Data Model tables that do not have partitions (that is, lookup tables and reference tables), to change the existing tablespace for a table then issue the following statement.

ALTER TABLE table_name MOVE TABLESPACE new_tablespace_name;

Data Compression in the Oracle Communications Data Model

A key decision that you must make is whether to compress your data. Using table compression reduces disk and memory usage, often resulting in better scale-up performance for read-only operations. Table compression can also speed up query execution by minimizing the number of round trips required to retrieve data from the disks. Compressing data however imposes a performance penalty on the load speed of the data.

Recommendations

In general, choose to compress the data. The overall performance gain typically outweighs the cost of compression.

If you decide to use compression, consider sorting your data before loading it to achieve the best possible compression rate. The easiest way to sort incoming data is to load it using an ORDER BY clause on either your CTAS or IAS statement. Specify an ORDER BY a NOT NULL column (ideally non numeric) that has a large number of distinct values (1,000 to 10,000).

See also:

"Types of Data Compression Available", "Example: Compressing an Oracle Communications Data Model Table", "Compressing Materialized Views", and "Exadata Hybrid Columnar Compression".

Types of Data Compression Available

Oracle Database offers three types of compression:

- Basic or Standard Compression. With standard compression Oracle Database compresses data by eliminating duplicate values in a database block. Standard compression only works for direct path operations (CTAS or IAS). If the data is modified using any kind of conventional DML operation (for example updates), the data within that database block is uncompressed to make the modifications and is written back to disk uncompressed.

- OLTP compression. OLTP compression is a component of the Advanced Compression option. With OLTP compression, just like standard compression, Oracle Database compresses data by eliminating duplicate values in a database block. But unlike standard compression OLTP compression allows data to remain compressed during all types of data manipulation operations, including conventional DML such as INSERT and UPDATE.

See:

Oracle Database Administrator's Guide for more information on OLTP table compression features.

Oracle by Example:

For more information on Oracle Advanced Compression, see the "Using Table Compression to Save Storage Costs" OBE tutorial.
To access the tutorial, open the Oracle Learning Library in your browser by following the instructions in "Oracle Technology Network"; and, then, search for the tutorial by name.

- Exadata Hybrid Columnar Compression (EHCC). EHCC achieves its compression using a different compression technique. A logical construct called the compression unit is used to store a set of Exadata Hybrid Columnar-compressed rows. When data is loaded, a set of rows is pivoted into a columnar representation and compressed. After the column data for a set of rows has been compressed, it is fit into the compression unit. If conventional DML is issued against a table with EHCC, the necessary data is uncompressed to do the modification and then written back to disk using a block-level compression algorithm. If your data set is frequently modified using conventional DML, then the use of EHCC is not recommended; instead, the use of OLTP compression is recommended.
Exadata Hybrid Columnar Compression (EHCC). EHCC provides different levels of compression, focusing on query performance or compression ratio respectively. With EHCC optimized for query, less compression algorithms are applied to the data to achieve good compression with little to no performance impact. However, compression for archive tries to optimize the compression on disk, irrespective of its potential impact on the query performance.

See also:

"Exadata Hybrid Columnar Compression"

Example: Compressing an Oracle Communications Data Model Table

By using a compression algorithm specifically designed for relational data, Oracle Database can compress data much more effectively than standard compression techniques. More significantly, unlike other compression techniques, Oracle Database incurs virtually no performance penalty for SQL queries accessing compressed tables.

Oracle Communications Data Model leverages the compress feature for all base, derived, and aggregate tables which reduces the amount of data being stored, reduces memory usage (more data per memory block), and increases query performance.

You can specify table compression by using the COMPRESS clause of the CREATE TABLE statement or you can enable compression for an existing table by using ALTER TABLE statement as shown below.

alter table <tablename> move compress;

For example, you could issue the following statements to create a compressed table named DWB_ACCS_MTHD_PORT_HIST.

COMPRESS
Create table DWB_ACCS_MTHD_PORT_HIST
(NP_RQST_HDR_CD                 VARCHAR2(30)
,ACCS_MTHD_KEY                   NUMBER(30)  NOT NULL ENABLE
,EXTRNL_OPRTR_KEY                NUMBER(30)
…..
)
tablespace TBS_BASE
COMPRESS ;

Tables for Supertype and Subtype Entities in Oracle Communications Data Model

A supertype is a generic entity type that has a relationship with one or more subtypes.

A subtype is a sub-grouping of the entities in an entity type that is meaningful to the organization and that shares common attributes or relationships distinct from other subgroups.

- Subtypes inherit all supertype attributes

- Subtypes have attributes that are different from other subtypes

Recommendations

Create separate tables for the super type and all sub type entities for the following reasons:

- Data integrity enforced at database level. (using NOT NULL column constraints)

- Relationships can be accurately modeled and enforced including those which apply to only one subtype

- Physical model closely resembles the logical data model.

- It is easier to correlate the logical data model with the physical data model and support the logical data model enhancements and changes.

- Physical data model reflects true business rules (for example, if there are some attributes or relationships mandatory for only one subtype.)

Surrogate Keys in the Physical Model

The surrogate key method for primary key construction involves taking the natural key components from the source systems and mapping them through a process of assigning a unique key value to each unique combination of natural key components (including source system identifier). The resulting primary key value is completely non-intelligent and is typically a numeric data type for maximum performance and storage efficiency.

Advantages of Surrogate keys include:

- Ensure uniqueness: data distribution

- Independent of source systems

- Re-numbering

- Overlapping ranges

- Uses the numeric data type which is the most performant data type for primary keys and joins

Disadvantages of Surrogate keys:

- Have to allocate during ETL

- Complex and expensive re-processing and data quality correction

- Not used in queries – performance impact

- The operational business intelligence requires natural keys to join to operational systems

Integrity Constraints in Oracle Communications Data Model

Integrity constraints are used to enforce business rules associated with your database and to prevent having invalid information in the tables. The most common types of constraints include:

- PRIMARY KEY constraints, this is usually defined on the surrogate key column to ensure uniqueness of the record identifiers. In general, it is recommended that you specify the ENFORCED ENABLED RELY mode.

- UNIQUE constraints, to ensure that a given column (or set of columns) is unique. For slowly changing dimensions, it is recommended that you add a unique constraint on the Business Key and the Effective From Date columns to allow tracking multiple versions (based on surrogate key) of the same Business Key record.

- NOT NULL constraints, to ensure that no null values are allowed. For query rewrite scenarios, it is recommended that you have an inline explicit NOT NULL constraint on the primary key column in addition to the primary key constraint.

- FOREIGN KEY constraints, to ensure that relation between tables are being honored by the data. Usually in data warehousing environments, the foreign key constraint is present in RELY DISABLE NOVALIDATE mode.

The Oracle Database uses constraints when optimizing SQL queries. Although constraints can be useful in many aspects of query optimization, constraints are particularly important for query rewrite of materialized views. Under some specific circumstances, constraints need space in the database. These constraints are in the form of the underlying unique index.

Unlike data in many relational database environments, data in a data warehouse is typically added or modified under controlled circumstances during the extraction, transformation, and loading (ETL) process.

Indexes and Partitioned Indexes in the Oracle Communications Data Model

Indexes are optional structures associated with tables or clusters. In addition to the classical B-tree indexes, bitmap indexes are very common in data warehousing environments

- Bitmap indexes are optimized index structures for set-oriented operations. Additionally, they are necessary for some optimized data access methods such as star transformations. Bitmap indexes are typically only a fraction of the size of the indexed data in the table.

- B-tree indexes are most effective for high-cardinality data: that is, for data with many possible values, such as customer_name or phone_number. However, fully indexing a large table with a traditional B-tree index can be prohibitively expensive in terms of disk space because the indexes can be several times larger than the data in the table. B-tree indexes can be stored specifically in a compressed manner to enable huge space savings, storing more keys in each index block, which also leads to less I/O and better performance.

Recommendations

Make the majority of the indexes in your customized Oracle Communications Data Model bitmap indexes.

Use B-tree indexes only for unique columns or other columns with very high cardinalities (that is, columns that are almost unique). Store the B-tree indexes in a compressed manner.

Partition the indexes. Indexes are just like tables in that you can partition them, although the partitioning strategy is not dependent upon the table structure. Partitioning indexes makes it easier to manage the data warehouse during refresh and improves query performance.

Typically, specify the index on a partitioned table as local. Bitmap indexes on partitioned tables must always be local. B-tree indexes on partitioned tables can be global or local. However, in a data warehouse environment, local indexes are more common than global indexes. Use global indexes only when there is a specific requirement which cannot be met by local indexes (for example, a unique index on a non-partitioning key, or a performance requirement).

See also:

"Partitioned Tables in the Oracle Communications Data Model", "Choosing Indexes for Materialized Views", "Choosing a Cube Partitioning Strategy for Oracle Communications Data Model", and "Partitioning and Materialized Views".

Partitioned Tables in the Oracle Communications Data Model

Partitioning allows a table, index or index-organized table to be subdivided into smaller pieces. Each piece of the database object is called a partition. Each partition has its own name, and may optionally have its own storage characteristics. From the perspective of a database administrator, a partitioned object has multiple pieces that can be managed either collectively or individually. This gives the administrator considerable flexibility in managing partitioned objects. However, from the perspective of the application, a partitioned table is identical to a non-partitioned table. No modifications are necessary when accessing a partitioned table using SQL DML commands.

As discussed in the following topics, partitioning can provide tremendous benefits to a wide variety of applications by improving manageability, availability, and performance:

- Partitioning the Oracle Communications Data Model for Manageability

- Partitioning the Oracle Communications Data Model for Easier Data Access

- Partitioning the Oracle Communications Data Model for Join Performance

Oracle by Example:

To understand the various partitioning techniques in Oracle Database, see the "Manipulating Partitions in Oracle Database 11g" OBE tutorial.
To access the tutorial, open the Oracle Learning Library in your browser by following the instructions in "Oracle Technology Network"; and, then, search for the tutorial by name.

See also:

"Indexes and Partitioned Indexes in the Oracle Communications Data Model", "Choosing a Cube Partitioning Strategy for Oracle Communications Data Model", and "Partitioning and Materialized Views".

Partitioning the Oracle Communications Data Model for Manageability

Range partitioning helps improve the manageability and availability of large volumes of data.

Consider the case where two year's worth of sales data or 100 terabytes (TB) is stored in a table. At the end of each day a new batch of data must be to loaded into the table and the oldest days worth of data must be removed. If the Sales table is range partitioned by day then the new data can be loaded using a partition exchange load. This is a sub-second operation that has little or no impact on end user queries.

Partitioning the Oracle Communications Data Model for Easier Data Access

Range partitioning also helps ensure that only the necessary data to answer a query is scanned. Assuming that the business users predominately accesses the sales data on a weekly basis (for example, total sales per week) then range partitioning this table by day ensures that the data is accessed in the most efficient manner, as only seven partitions must be scanned to answer the business users query instead of the entire table. The ability to avoid scanning irrelevant partitions is known as partition pruning.

Partitioning the Oracle Communications Data Model for Join Performance

Sub-partitioning by hash is used predominately for performance reasons. Oracle Database uses a linear hashing algorithm to create sub-partitions. In order to ensure that the data gets evenly distributed among the hash partitions it is highly recommended that the number of hash partitions is a power of 2 (for example, 2, 4, 8, and so on). A good rule of thumb to follow when deciding the number of hash partitions a table should have is 2 X # of CPUs rounded to up to the nearest power of 2.

If your system has 12 CPUs, then 32 would be a good number of hash partitions. On a clustered system the same rules apply. If you have 3 nodes each with 4 CPUs, then 32 would still be a good number of hash partitions. However, ensure that each hash partition is at least 16MB in size. Many small partitions do not have efficient scan rates with parallel query. Consequently, if using the number of CPUs makes the size of the hash partitions too small, use the number of Oracle RAC nodes in the environment (rounded to the nearest power of 2) instead.

A major performance benefit of hash partitioning is partition-wise joins. Partition-wise joins reduce query response time by minimizing the amount of data exchanged among parallel execution servers when joins execute in parallel. This significantly reduces response time and improves both CPU and memory resource usage. In a clustered data warehouse, this significantly reduces response times by limiting the data traffic over the interconnect (IPC), which is the key to achieving good scalability for massive join operations. Partition-wise joins can be full or partial, depending on the partitioning scheme of the tables to be joined.

A full partition-wise join divides a join between two large tables into multiple smaller joins. Each smaller join, performs a joins on a pair of partitions, one for each of the tables being joined. For the optimizer to choose the full partition-wise join method, both tables must be equi-partitioned on their join keys. That is, they have to be partitioned on the same column with the same partitioning method. Parallel execution of a full partition-wise join is similar to its serial execution, except that instead of joining one partition pair at a time, multiple partition pairs are joined in parallel by multiple parallel query servers. The number of partitions joined in parallel is determined by the Degree of Parallelism (DOP).

Figure 3-2 Partitioning for Join Performance

Parallel Execution in the Oracle Communications Data Model

Parallel Execution enables a database task to be parallelized or divided into smaller units of work, thus allowing multiple processes to work concurrently. By using parallelism, a terabyte of data can be scanned and processed in minutes or less, not hours or days.

Figure 3-3, "Parallel execution of a full partition-wise join between two tables" illustrates the parallel execution of a full partition-wise join between two tables, Sales and Customers. Both tables have the same degree of parallelism and the same number of partitions. They are range partitioned on a date field and sub partitioned by hash on the cust_id field. As illustrated in the picture, each partition pair is read from the database and joined directly.

There is no data redistribution necessary, thus minimizing IPC communication, especially across nodes. Below figure shows the execution plan you would see for this join.

Figure 3-3 Parallel execution of a full partition-wise join between two tables

To ensure that you get optimal performance when executing a partition-wise join in parallel, specify a number for the partitions in each of the tables that is larger than the degree of parallelism used for the join. If there are more partitions than parallel servers, each parallel server is given one pair of partitions to join, when the parallel server completes that join, it requests another pair of partitions to join. This process repeats until all pairs have been processed. This method enables the load to be balanced dynamically (for example, 128 partitions with a degree of parallelism of 32).

What happens if only one table that you are joining is partitioned? In this case the optimizer could pick a partial partition-wise join. Unlike full partition-wise joins, partial partition-wise joins can be applied if only one table is partitioned on the join key. Hence, partial partition-wise joins are more common than full partition-wise joins. To execute a partial partition-wise join, Oracle Database dynamically repartitions the other table based on the partitioning strategy of the partitioned table.

After the other table is repartitioned, the execution is similar to a full partition-wise join. The redistribution operation involves exchanging rows between parallel execution servers. This operation leads to interconnect traffic in Oracle RAC environments, since data must be repartitioned across node boundaries.

Figure 3-4 Partial Partition-wise Join

Figure 3-4, "Partial Partition-wise Join" illustrates a partial partition-wise join. It uses the same example as in former Figure, except that the customer table is not partitioned. Before the join operation is executed, the rows from the customers table are dynamically redistributed on the join key.

- Enabling Parallel Execution for a Session

- Enabling Parallel Execution of DML Operations

- Enabling Parallel Execution at the Table Level

Enabling Parallel Execution for a Session

Parallel query is the most commonly used parallel execution feature in Oracle Database. Parallel execution can significantly reduce the elapsed time for large queries. To enable parallelization for an entire session, execute the following statement.

alter session enable parallel query;

Enabling Parallel Execution of DML Operations

Data Manipulation Language (DML) operations such as INSERT, UPDATE, and DELETE can be parallelized by Oracle Database. Parallel execution can speed up large DML operations and is particularly advantageous in data warehousing environments. To enable parallelization of DML statements, execute the following statement.

alter session enable parallel dml;

When you issue a DML statement such as an INSERT, UPDATE, or DELETE, Oracle Database applies a set of rules to determine whether that statement can be parallelized. The rules vary depending on whether the statement is a DML INSERT statement, or a DML UPDATE or DELETE statement.

- The following rules apply when determining how to parallelize DML UPDATE and DELETE statements:

- Oracle Database can parallelize UPDATE and DELETE statements on partitioned tables, but only when multiple partitions are involved.

- You cannot parallelize UPDATE or DELETE operations on a non-partitioned table or when such operations affect only a single partition.

- The following rules apply when determining how to parallelize DML INSERT statements:

- Standard INSERT statements using a VALUES clause cannot be parallelized.

- Oracle Database can parallelize only INSERT . . . SELECT . . . FROM statements.

Enabling Parallel Execution at the Table Level

The setting of parallelism for a table influences the optimizer. Consequently, when using parallel query, also enable parallelism at the table level by issuing the following statement.

alter table <table_name> parallel 32;

4 Access Layer Customization

This chapter provides information about customizing the access layer of Oracle Communications Data Model. It includes the following topics:

- Introduction to Customizing the Access Layer of Oracle Communications Data Model

- Derived Tables in the Oracle Communications Data Model

- Aggregate Tables in the Oracle Communications Data Model

- Dimensional Components in the Oracle Communications Data Model

- Materialized Views in the Oracle Communications Data Model

See also:

Chapter 3, "Physical Model Customization"

Introduction to Customizing the Access Layer of Oracle Communications Data Model

The access layer of Oracle Communications Data Model provides the calculated and summarized ("flattened") perspectives of the data needed by business intelligence tools. Access layer objects are populated using the data from the foundation layer 3NF objects.

The access layer objects in the ocdm_sys schema include: derived and aggregate tables, OLAP cubes, and materialized views. This layer also contains data mining models. The results of the these models are stored in derived tables. The models themselves are defined in the ocdm_mining schema.

When designing and customizing access layer objects:

- Follow the general guidelines for customizing physical objects given in "General Recommendations When Designing Physical Structures".

- Design the access layer objects to support the business intelligence reports and queries that your site makes. See Chapter 6, "Report and Query Customization."

The following topics provide specialized information about designing and customizing access layer objects:

- Derived Tables in the Oracle Communications Data Model

- Aggregate Tables in the Oracle Communications Data Model

- Dimensional Components in the Oracle Communications Data Model

- Materialized Views in the Oracle Communications Data Model

Derived Tables in the Oracle Communications Data Model

Derived tables are tables that have as values the result of a non-aggregate calculation against the data in the foundation layer tables. Derived tables have a DWD_ prefix.

There are two main types of derived tables in the default Oracle Communications Data Model and the way you customize these tables varies by type:

- Tables that hold the results of a calculation such as the DWD_BER_FER_ERR_RATIO_DAY table that contains values that are the daily BER (Bit Error Rate) and FER (Frame Error Rate) statistics about the network elements. For information on customizing these tables, see "Creating New Derived Tables for Calculated Data".

- Result tables for the data mining models (for example, DWD_CUST_MNNG). For information on customizing data mining models, see "Customizing Oracle Communications Data Model Data Mining Models".

See:

The Derived Tables topic in Oracle Communications Data Model Reference for a list of all of the derived tables in the default Oracle Communications Data Model. For a list of only those derived tables that are results tables for the data mining models, see the chapter on Data Mining Models in Oracle Communications Data Model Reference.

Creating New Derived Tables for Calculated Data

If, during fit-gap analysis, you identified a need for calculated data that is not provided by the default derived tables, you can meet this need by defining new tables. When designing these tables, name the tables following the convention of using the DWD_ prefix for derived tables.

Customizing Oracle Communications Data Model Data Mining Models

Some derived (DWD_) tables in the default ocdm_sys schema are the results of data mining models defined in the default Oracle Communications Data Model. Those models are defined in the default ocdm_mining schema that also comes with Oracle Communications Data Model.

All Oracle Communications Data Model mining models use materialized views defined in the ocdm_mining schema as source input. Those materialized views are defined in ocdm_mining_etl.sql file in $ORACLE_HOME/ocdm/pdm/mining/src. Different mining models use different source materialized views.

When creating a customized Oracle Communications Data Model warehouse, you can customize the data mining models in the following ways:

- Create a new model as discussed in "Creating a New Data Mining Model for Oracle Communications Data Model".

- Modify an existing model as discussed in "Modifying Oracle Communications Data Model Data Mining Models".

See also:

"Tutorial: Customizing the Churn Prediction Mining Model".

Creating a New Data Mining Model for Oracle Communications Data Model

To write a new data mining model:

1. Ensure that the ocdm_mining schema includes a definition for a materialized view that you can use as input to the model. Define a new materialized view, if necessary.

2. Create the model as you would any data mining model. Follow the instructions given in Oracle Data Mining Concepts. Add the model to the ocdm_mining schema.

3. Add any physical tables needed by the model into the ocdm_sys schema. Follow the naming conventions outlined in "Conventions When Customizing the Physical Model" and use a DWD_ prefix for results tables.

4. In the ocdm_mining schema, grant SELECT privileges to the results tables created in Step 3.

5. Modify the intra-ETL to support the use of the data mining model.

Modifying Oracle Communications Data Model Data Mining Models

To customize Oracle Communications Data Model mining models, take the following steps:

1. Change the definition for source materialized views used as input to the mining model.
For example, the activity create_churn_svm_model uses the DMV_CUST_CHRN_SRC_PRD source materialized view. To add a new column into this model, modify the following materialized views to add a new column:

DMV_CUST_CHRN_SRC_SRC
DMV_CUST_CHRN_SRC_PRD
DMV_CUST_CHRN_SRC_TST
DMV_CUST_CHRN_APPLY_ALL

2. Train the model again by calling Oracle Communications Data Model mining package.
For example, to train the churn svm model, execute the following statement.

ocdm_mining.create_churn_svm_model( MONTH_CODE );

3. Ensure that the new column has been added into the model.
For example, use the following statement to query the result table and ensure the new column name is included in the query result:

SELECT attribute_name FROM TABLE(Oracle Communications Data Model
SELECT ATTRIBUTE_SET ROM TABLE(DBMS_DATA_MINING.GET_MODEL_DETAILS_SVM('OCDM_CHURN_SVM'))
WHERE CLASS='1' );

See also:

"Refreshing Oracle Communications Data Model Data Mining Models", "Troubleshooting Data Mining Model Creation", and Appendix D, "Working with User Privileges in Oracle Communications Data Model."

Tutorial: Customizing the Churn Prediction Mining Model

After you have populated Oracle Communications Data Model foundation layer and the derived tables, you can leverage the prebuilt Oracle Communications Data Model mining model for some more advanced analysis and predictions.

This tutorial shows you how to predict the customers who will terminate the service in next 3 months (churners) based on the populated Oracle Communications Data Model warehouse. Using prebuilt Oracle Communications Data Model Mining models, you can easily and very quickly see the prediction result of your customers, without having to go through all of the data preparation, training, testing and applying process that you must perform in a traditional from-scratch mining project.

See:

Oracle Data Mining Concepts for more information about the Oracle Database mining training and scoring (applying) process.

After the initially generating a mining model, as time goes by, the customer information and their behavior change. Consequently, you must refresh the previous trained mining models based on the latest customer and usage data. You can follow the process in this tutorial to refresh the mining models to acquire predictions on latest customer information.

This tutorial shows you how to generate the Churn Prediction model through Oracle Communications Data Model Mining APIs. To use different parameters in the training process, or customize the model in more advanced fashion, you can also use Oracle Data Miner to do the same work.

This tutorial consists of the following:

- Tutorial Prerequisites

- Preparing Your Environment

- Generating Churn Prediction Model

- Checking the Result

Tutorial Prerequisites

Before starting this tutorial:

1. Review the Oracle by Example (OBE) tutorial "Using Oracle Data Miner 11g Release 2." To access this tutorial, open the Oracle Learning Library in your browser by following the instructions in "Oracle Technology Network"; and, then, search for the tutorial by name.

2. Install Oracle Communication Data Model.

3. Populate the base, reference and lookup tables.

4. Execute the intra-ETL.
Ensure that at least the following tables contain valid data:

DWD_ACCT_STTSTC

DWD_VAS_SBRP_QCK_SUMM

DWR_BSNS_MO

DWR_CUST

DWR_HH

DWR_JB

Note:

If you have not populated the real customer data, and only want to learn the Oracle Communications Data Model mining model, you can use the sample data by taking the following steps:

1. Ensure that during the install, you generated the calendar data covering range of 2007~2008. For example, the parameters of starting from "20070101" for 5 years satisfy this condition

2. Download the sample data(ocdm.4.mining.dmp.gz) and import that data into your new ocdm_sys schema.

Preparing Your Environment

This tutorial requires a valid, populated Oracle Communications Data Model warehouse.

Oracle by Example:

For more information about using SQL Developer, refer to tutorial "Getting Started with Oracle SQL Developer 3.0". To access this tutorial, open the Oracle Learning Library in your browser by following the instructions in "Oracle Technology Network"; and, then, search for the tutorial by name.

To prepare the environment, take the following steps:

1. In SQL Developer, connect to the ocdm_sys and the ocdm_mining schemas.

Tip:

SQL Developer can be found on any Oracle Database Installation under $ORACLE_HOME/sqldeveloper.

2. After you connect to the ocdm_sys schema, you can see all the tables in that schema. You can narrow down the list by right clicking the "table" and then applying filters:

3. (Optional) As mentioned in the "Preparing Your Environment", if you have not populated those tables with your own data, you can try with some sample data. After you download the sample data, follow those steps to import the data:

1. Grant dba to ocdm_sys by issuing the following statement:

grant dba to ocdm_sys.

2. Disable all foreign keys on those tables used by the tutorial:

ALTER TABLE "DWR_CUST" DISABLE CONSTRAINT "CI_CS5_FK".
ALTER TABLE "DWR_CUST" DISABLE CONSTRAINT "CI_POT_FK".
ALTER TABLE "DWR_CUST" DISABLE CONSTRAINT "CI_DLCS_FK".
ALTER TABLE "DWR_CUST" DISABLE CONSTRAINT "CI_SJ_FK".
ALTER TABLE "DWR_CUST" DISABLE CONSTRAINT "CI_SEG_FK".
ALTER TABLE "DWR_CUST" DISABLE CONSTRAINT "CI_DLCT_FK".
ALTER TABLE "DWR_CUST" DISABLE CONSTRAINT "CI_LAN_FK".
ALTER TABLE "DWR_CUST" DISABLE CONSTRAINT "CI_AL1_FK".
ALTER TABLE "DWR_CUST" DISABLE CONSTRAINT "CI_PT_FK".
ALTER TABLE "DWR_CUST" DISABLE CONSTRAINT "CI_BLC_FK".
ALTER TABLE "DWR_CUST" DISABLE CONSTRAINT "CI_DLMS_FK".
ALTER TABLE "DWR_CUST" DISABLE CONSTRAINT "CI_DLJ_FK".
ALTER TABLE "DWR_CUST" DISABLE CONSTRAINT "CI_DLG_FK".
ALTER TABLE "DWR_CUST" DISABLE CONSTRAINT "CI_HOU1_FK".
ALTER TABLE "DWR_CUST" DISABLE CONSTRAINT "CI_DLE_FK".
ALTER TABLE "DWR_CUST" DISABLE CONSTRAINT "CI_DLN_FK".
ALTER TABLE "DWR_CUST" DISABLE CONSTRAINT "CI_OI_FK".
ALTER TABLE "DWD_ACCT_STTSTC" DISABLE CONSTRAINT "ACS_CI_FK".
ALTER TABLE "DWD_ACCT_STTSTC" DISABLE CONSTRAINT "ACS_BM_FK".
ALTER TABLE "DWD_ACCT_STTSTC" DISABLE CONSTRAINT "ACS_RSCR_FK".
ALTER TABLE "DWD_ACCT_STTSTC" DISABLE CONSTRAINT "ACS_AL1_FK".
ALTER TABLE "DWD_ACCT_STTSTC" DISABLE CONSTRAINT "ACS_DAB_FK".
ALTER TABLE "DWD_ACCT_STTSTC" DISABLE CONSTRAINT "ACS_DLCRB_FK".
ALTER TABLE "DWD_ACCT_STTSTC" DISABLE CONSTRAINT "ACS_DLCC_FK".
ALTER TABLE "DWD_ACCT_STTSTC" DISABLE CONSTRAINT "ACS_DAS_FK".
ALTER TABLE "DWD_ACCT_STTSTC" DISABLE CONSTRAINT "ACS_PM_FK".
ALTER TABLE "DWD_ACCT_STTSTC" DISABLE CONSTRAINT "ACS_DOBU_FK".
ALTER TABLE "DWD_ACCT_STTSTC" DISABLE CONSTRAINT "ACS_CR1_FK".
ALTER TABLE "DWD_ACCT_STTSTC" DISABLE CONSTRAINT "ACS_LAB_FK".
ALTER TABLE "DWD_ACCT_STTSTC" DISABLE CONSTRAINT "ACS_ACCT_FK".
ALTER TABLE "DWD_ACCT_STTSTC" DISABLE CONSTRAINT "ACS_CHR_FK".
ALTER TABLE "DWD_ACCT_STTSTC" DISABLE CONSTRAINT "ACS_DLAON_FK".
ALTER TABLE "DWD_ACCT_STTSTC" DISABLE CONSTRAINT "ACS_DLCT_FK1".
ALTER TABLE "DWD_ACCT_STTSTC" DISABLE CONSTRAINT "ACS_GEOC_FK1".
ALTER TABLE "DWD_VAS_SBRP_QCK_SUMM" DISABLE CONSTRAINT "VS_AM_FK".
ALTER TABLE "DWD_VAS_SBRP_QCK_SUMM" DISABLE CONSTRAINT "VS_CI_FK".
ALTER TABLE "DWD_VAS_SBRP_QCK_SUMM" DISABLE CONSTRAINT "VS_BM_FK".
ALTER TABLE "DWD_VAS_SBRP_QCK_SUMM" DISABLE CONSTRAINT "VS_DLCT_FK1".

3. Import the sample dump into ocdm_sys schema by issuing the following statement.

imp ocdm_sys/ocdm_sys@tnsname file=ocdm.4.mining.dmp full=y ignore=y

4. Review tables to ensure they contain valid data, either from your own customer data, or from the sample data.

5. Review the DWR_CUST table.

6. Click each of the following tables to ensure that the table is properly populated:

DWD_ACCT_STTSTC

DWD_VAS_SBRP_QCK_SUMM

DWR_BSNS_MO

DWR_CUST

DWR_HH

DWR_JB

7. Check that the DWD_CUST_MNNG and DWD_CUST_PROD_AFFLTN results tables are empty before running the model generation function as described in "Generating Churn Prediction Model".

Generating Churn Prediction Model

This tutorial uses two procedures from Oracle Communications Data Model Mining APIs:

- pkg_ocdm_mining.refresh_mining_source that populates some mining source tables from the ocdm_sys schema tables.

- pkg_ocdm_mining.create_churn_svm_model that trains the model, and generates the result of prediction.

Take the following steps to use the procedures:

1. Refresh the Oracle Communications Data Model mining source tables by executing the following SQL statements.

select count (*) from dmv_cust_chrn_src_all;
exec pkg_ocdm_mining.refresh_mining_source;
select count (*) from dmv_cust_chrn_src_all;

These statements display the number of tables before refreshing.

Refresh the tables and rexecute the statements to display the number of tables after refreshing.

2. Generate the Churn Prediction model by executing the following SQL statements.

select count(*) from dwd_cust_mnng;
select count(*) from dwd_chrn_svm_factor;
exec pkg_ocdm_mining.create_churn_svm_model;
select count(*) from dwd_cust_mnng;

These statements:

- Show the record counts before generation.

- Generate the mode.

- Show the count after generation.

Note:

This tutorial does not refresh all models. It only refreshes one churn prediction model. To refresh all of the default mining models based on latest customer data, follow the instructions in "Refreshing Oracle Communications Data Model Data Mining Models".

Checking the Result

After you have refreshed, trained, and generated the model, check the DWD_CUST_MNNG results table in ocdm_sys schema by taking the following steps:

1. Issue the following query.

Select cust_key, prdct_churn_svm_ind, prdct_churn_svm_prob From dwd_cust_mnng where rownum < 10;

2. For each customer identified by CUST_KEY, the PRDCT_CHRN_SVM_IND column gives a Boolean prediction of whether a customer will churn in next 3 months. Zero (0) stands for non-churner, while one (1) stands for churner. The PRDCT_CHURN_SVM_PROB column provides a more detailed probability (0~1) of how likely a customer is going to churn.

3. (Optional) If you have also installed the Oracle Communications Data Model sample reports in Oracle Business Intelligence Suite Enterprise Edition 11g, you can also view the results as an Oracle Business Intelligence Suite Enterprise Edition report.

See:

Oracle Communications Data Model Installation Guide for more information on installing the sample reports and deploying the Oracle Communications Data Model RPD and webcat on the Business Intelligence Suite Enterprise Edition instance.

In the Oracle Business Intelligence Suite Enterprise Edition report, the customer revenue information such as the contract ARPU, debt value is shown combined with the prediction results.

Aggregate Tables in the Oracle Communications Data Model

Aggregate tables are tables that aggregate or "roll up" the data to one level higher than a base or derived table. The aggregate tables in the default Oracle Communications Data Model are actually materialized views and have a DWA_ prefix. These aggregate tables provide a view of the data similar to the view provided by a fact table in a snowflake schema.

The default Oracle Communications Data Model defines several aggregate tables. For example, the DWA_BER_FER_ERR_RATIO_MONTH table aggregates the values of the DWD_BER_FER_ERR_RATIO_DAY table to the month level.

See:

The "Aggregate Tables" topic in "Oracle Communications Data Model Reference for a list of the aggregate tables in the default Oracle Communications Data Model.

If, during fit-gap analysis, you identified a need for simple aggregated data that is not provided by the default aggregate tables, you can define new materialized views. When designing these tables, keep the following points in mind:

- Create a query for the materialized view that aggregates up only a single level. For example, if aggregating over time, then aggregate only from day to month.

Note:

When you must aggregate up many levels (for example in time, month, quarter, and year) or different hierarchies (for example, the fiscal and calendar hierarchies for the time dimension), do not define a DWA_ table; instead, define the aggregations by creating OLAP cubes.

See also:

"Materialized Views in the Oracle Communications Data Model" and "Defining New Oracle OLAP Cubes for Oracle Communications Data Model".

- Name the tables following the conventions outlined in "General Naming Conventions for Physical Objects" and use an DWA_ prefix.

Dimensional Components in the Oracle Communications Data Model

There is often much discussion regarding the 'best' modeling approach to take for any given data warehouse with each style, classic 3NF and dimensional having their own strengths and weaknesses. It is likely that data warehouses must do more to embrace the benefits of each model type rather than rely on just one - this is the approach that was adopted in designing the Oracle Communications Data Model. The foundation layer of the Oracle Communications Data Model is a 3NF model. The default Oracle Communications Data Model also provides a dimensional model of the data. This dimensional model of the data is a perspective that summarizes and aggregates data, rather than preserving detailed transaction information.

Familiarize yourself with dimensional modeling by reading the following topics before you begin to customize the dimensional model of the default Oracle Communications Data Model:

- Characteristics of a Dimensional Model

- Characteristics of Relational Star and Snowflake Tables

- Characteristics of the OLAP Dimensional Model

- Characteristics of the OLAP Cubes in Oracle Communications Data Model

- Defining New Oracle OLAP Cubes for Oracle Communications Data Model

- Changing an Oracle OLAP Cube in Oracle Communications Data Model

- Creating a New Forecast Cube in Oracle Communications Data Model

- Choosing a Cube Partitioning Strategy for Oracle Communications Data Model

- Choosing a Cube Data Maintenance Method for Oracle Communications Data Model

Characteristics of a Dimensional Model

The simplicity of a dimensional model is inherent because it defines objects that represent real-world business entities. Analysts know which business measures they are interested in examining, which dimensions and attributes make the data meaningful, and how the dimensions of their business are organized into levels and hierarchies.

In the simplest terms, a dimensional model identifies the following objects:

- Measures. Measures store quantifiable business data (such as sales, expenses, and inventory). Measures are sometimes called "facts". Typical examples of measures are sales, cost, expenses, inventory profit, and margin. Measures are organized by one or more dimensions and may be stored or calculated at query time:

- Stored Measures. Stored measures are loaded and stored at the leaf level. Commonly, there is also a percentage of summary data that is stored. Summary data that is not stored is dynamically aggregated when queried.

- Calculated Measures. Calculated measures are measures whose values are calculated dynamically at query time. Only the calculation rules are stored in the database. Common calculations include measures such as ratios, differences, moving totals, and averages. Calculations do not require disk storage space, and they do not extend the processing time required for data maintenance.

- Dimensions. A dimension is a structure that categorizes data to enable users to answer business questions. Commonly used dimensions are Customers, Products, and Time. A dimension's structure is organized hierarchically based on parent-child relationships. These relationships enable:

- Navigation between levels.
Hierarchies on dimensions enable drilling down to lower levels or navigation (rolling up) to higher levels. Drilling down on the Time dimension member 2005 typically navigates you to the quarters Q1 2005 through Q4 2005. In a calendar year hierarchy, drilling down on Q1 2005 would navigate you to the months, January 05 through March 05. These kinds of relationships make it easy for users to navigate large volumes of multidimensional data.

- Aggregation from child values to parent values.
The parent represents the aggregation of its children. Data values at lower levels aggregate into data values at higher levels. Dimensions are structured hierarchically so that data at different levels of aggregation are manipulated efficiently for analysis and display.

- Allocation from parent values to child values.
The reverse of aggregation is allocation and is heavily used by planning budgeting, and similar applications. Here, the role of the hierarchy is to identify the children and descendants of particular dimension members of "top-down" allocation of budgets (among other uses).

- Grouping of members for calculations.
Share and index calculations take advantage of hierarchical relationships (for example, the percentage of total profit contributed by each product, or the percentage share of product revenue for a certain category, or costs as a percentage of the geographical region for a retail location).

A dimension object helps to organize and group dimensional information into hierarchies. This represents natural 1:n relationships between columns or column groups (the levels of a hierarchy) that cannot be represented with constraint conditions. Going up a level in the hierarchy is called rolling up the data and going down a level in the hierarchy is called drilling down the data.

There are two ways that you can implement a dimensional model:

- Relational tables in a star schema configuration. This traditional method of implementing a dimensional model is discussed in "Characteristics of Relational Star and Snowflake Tables".

- Oracle OLAP Cubes. The physical model provided with Oracle Communications Data Model provides a dimensional perspective of the data using Oracle OLAP cubes. This dimensional model is discussed in "Characteristics of the OLAP Dimensional Model".

Characteristics of Relational Star and Snowflake Tables

In the case of relational tables, the dimensional model has historically been implemented as a star or snowflake schema. Dimension tables (which contain information about hierarchies, levels, and attributes) join to one or more fact tables. Fact tables are the large tables that store quantifiable business measurements (such as sales, expenses, and inventory) and typically have foreign keys to the dimension tables. Dimension tables, also known as lookup or reference tables. contain the relatively static or descriptive data in the data warehouse.

A star schema borders on a physical model, as drill paths, hierarchy and query profile are embedded in the data model itself rather than the data. This in part at least, is what makes navigation of the model so straightforward for end users. Star schemas usually have a large fact table surrounded by smaller dimension tables. Dimension tables do not change very much. Most of the information that the users need are in the fact tables. Therefore, star schemas have fewer table joins than do 3NF models.

A star schema is so called because the diagram resembles a star, with points radiating from a center. The center of the star consists of one or more fact tables and the points of the star are the dimension tables.

Figure 4-1 Star Schema Diagram

Snowflake schemas are slight variants of a simple star schema where the dimension tables are further normalized and broken down into multiple tables. The snowflake aspect only affects the dimensions and not the fact table and is therefore considered conceptually equivalent to star schemas. Snowflake dimensions are useful and indeed necessary when there are fact tables of differing granularity. A month-level derived or aggregate table (or materialized view) must be associated with a month level snowflake dimension table rather than the default (lower) Day level star dimension table.

See also:

"Declaring Relational Dimension Tables" and "Validating Relational Dimension Tables".

Declaring Relational Dimension Tables

When a relational table acts as a dimension to a fact table, it is recommended that you declare that table as a dimension (even though it is not necessary). Defined dimensions can yield significant performance benefits, and support the use of more complex types of rewrite.

To define and declare the structure of the dimension use the CREATE DIMENSION command. Use the LEVEL clause to identify the names of the dimension levels.

Validating Relational Dimension Tables

To improve the data quality of the dimension data in the data warehouse, it is recommended that you validate the declarative information about the relationships between the dimension members after any modification to the dimension data.

To perform this validation, use the VALIDATE_DIMENSION procedure of the DBMS_DIMENSION package. When the VALIDATE_DIMENSION procedure encounters any errors, the procedure places the errors into the DIMENSION_EXCEPTIONS table. To find the exceptions identified by the VALIDATE_DIMENSION procedure, query the DIMENSION_EXCEPTIONS table.

You can schedule a call to the VALIDATE_DIMENSION procedure as a post-process step to the regular Incremental Dimension load script. This can be done before the call to refresh the derived or aggregate tables of the data model through materialized view refresh, intra-ETL package calls.

Characteristics of the OLAP Dimensional Model

Oracle OLAP Cubes logically represent data similar to relational star tables, although the data is actually stored in multidimensional arrays. Like dimension tables, cube dimensions organize members into hierarchies, levels, and attributes. The cube stores the measure (fact) data. The dimensions form the edges of the cube.

Oracle OLAP is an OLAP server embedded in the Oracle Database. Oracle OLAP provides native multidimensional storage and speed-of-thought response times when analyzing data across multiple dimensions. The database provides rich support for analytics such as time series calculations, forecasting, advanced aggregation with additive and nonadditive operators, and allocation operations.

By integrating multidimensional objects and analytics into the database, Oracle provides the best of both worlds: the power of multidimensional analysis along with the reliability, availability, security, and scalability of the Oracle database.

Oracle OLAP is fully integrated into Oracle Database. At a technical level, this means:

- The OLAP engine runs within the kernel of Oracle Database.

- Dimensional objects are stored in Oracle Database in their native multidimensional format.

- Cubes and other dimensional objects are first class data objects represented in the Oracle data dictionary.

- Data security is administered in the standard way, by granting and revoking privileges to Oracle Database users and roles.

- OLAP cubes, dimensions, and hierarchies are exposed to applications as relational views. Consequently, applications can query OLAP objects using SQL as described in "Oracle OLAP Cube Views" and Chapter 6, "Report and Query Customization."

- Oracle OLAP cubes can be enhanced so that they are materialized views as described in "Cube Materialized Views".

See also:

Oracle OLAP User's Guide and"Characteristics of the OLAP Cubes in Oracle Communications Data Model".

Benefits of Using Oracle OLAP

The benefits to your organization are significant. Oracle OLAP offers the power of simplicity: One database, standard administration and security, standard interfaces and development tools.

The Oracle OLAP dimensional data model is highly structured. Structure implies rules that govern the relationships among the data and control how the data can be queried. Cubes are the physical implementation of the dimensional model, and thus are highly optimized for dimensional queries. The OLAP engine leverages this innate dimensionality in performing highly efficient cross-cube joins for inter-row calculations, outer joins for time series analysis, and indexing. Dimensions are pre-joined to the measures. The technology that underlies cubes is based on an indexed multidimensional array model, which provides direct cell access.

The OLAP engine manipulates dimensional objects in the same way that the SQL engine manipulates relational objects. However, because the OLAP engine is optimized to calculate analytic functions, and dimensional objects are optimized for analysis, analytic and row functions can be calculated much faster in OLAP than in SQL.

The dimensional model enables Oracle OLAP to support high-end business intelligence tools and applications such as OracleBI Discoverer Plus OLAP, OracleBI Spreadsheet Add-In, Oracle Business Intelligence Suite Enterprise Edition, BusinessObjects Enterprise, and Cognos ReportNet.

Oracle OLAP Dimensional Objects

Oracle OLAP dimensional objects include cubes, measures, dimensions, hierarchies, levels and attributes. The OLAP dimensional objects are described in detail in Oracle OLAP User's Guide. Figure 4-2 shows the general relationships among the objects.

Figure 4-2 Diagram of the OLAP Dimensional Model

Oracle OLAP Cube Views

When you define an OLAP cube, Oracle OLAP automatically generates a set of relational views on the cube and its dimensions and hierarchies

- Cube view. Each cube has a cube view that presents the data for all the measures and calculated measures in the cube. You can use a cube view like a fact table in a star or snowflake schema. However, the cube view contains all the summary data in addition to the detail level data. The default name of a cube view is cube_VIEW.

- Dimension and hierarchy views. Each dimension has one dimension view plus a hierarchy view for each hierarchy associated with the dimension. The default name for a dimension view is dimension_VIEW. For a hierarchy view, the default name is dimension_hierarchy_VIEW.

These views are related in the same way as fact and dimension tables in a star schema. Cube views serve the same function as fact tables, and hierarchy views and dimension views serve the same function as dimension tables. Typical queries join a cube view with either a hierarchy view or a dimension view.

SQL applications query these views to display the information-rich contents of these objects to analysts and decision makers. You can also create custom views that follow the structure expected by your applications, using the system-generated views like base tables.

See also:

The discussion on querying dimensional objects in Oracle OLAP User's Guide and Chapter 6, "Report and Query Customization."

Cube Materialized Views

Oracle OLAP cubes can be enhanced so that they are materialized views. A cube that has been enhanced in this way is called a cube materialized view and has a CB$ prefix. Cube materialized views can be incrementally refreshed through the Oracle Database materialized view subsystem, and they can serve as targets for transparent rewrite of queries against the source tables.

The OLAP dimensions associated with a cube materialized view are also defined with materialized view capabilities.

Necessary Cube Characteristics for Cube Materialized Views

A cube must conform to these requirements, before it can be designated as a cube materialized view:

- All dimensions of the cube have at least one level and one level-based hierarchy. Ragged and skip-level hierarchies are not supported. The dimensions must be mapped.

- All dimensions of the cube use the same aggregation operator, which is either SUM, MIN, or MAX.

- The cube has one or more dimensions and one or more measures.

- The cube is fully defined and mapped. For example, if the cube has five measures, then all five are mapped to the source tables.

- The data type of the cube is NUMBER, VARCHAR2, NVARCHAR2, or DATE.

- The source detail tables support dimension and rely constraints. If they have not been defined, then use the Relational Schema Advisor to generate a script that defines them on the detail tables.

- The cube is compressed.

- The cube can be enriched with calculated measures, but it cannot support more advanced analytics in a cube script.

Adding Materialized View Capabilities

To add materialized view capabilities to an OLAP cube, take the following steps:

1. In the Analytic Workspace Manager, connect to the ocdm_sys schema.

2. From the cube list, select the cube which you want to enable.

3. In the right pane, select the Materialized Views tab.

4. Select Enable Materialized View Refresh of the Cube. then click Apply.

Note:

You cannot enable the cube materialized view for a forecast cube.

Oracle by Example:

For more information on working with OLAP cubes, see the following OBE tutorials:

- "Querying OLAP 11g Cubes"

- "Using Oracle OLAP 11g With Oracle BI Enterprise Edition"

To access the tutorials, open the Oracle Learning Library in your browser by following the instructions in "Oracle Technology Network"; and, then, search for the tutorials by name.

See also:

Oracle OLAP User's Guide

Characteristics of the OLAP Cubes in Oracle Communications Data Model

The default access layer of Oracle Communications Data Model provides a dimensional perspective of the data using Oracle OLAP cubes.

There are OLAP cubes defined in the default ocdm_sys schema. These cubes have the general characteristics described in "Characteristics of the OLAP Dimensional Model". Specifically, OLAP cubes in the Oracle Communications Data Model have the following characteristics:

- All of the default OLAP cubes are loaded with data from DWA_ tables that are materialized views.

- The cubes were defined and built using the Analytical Workspace Manager (AWM) client tool.

- A relational view (with a _VIEW suffix) is defined over each of the OLAP cubes.

- All of the OLAP cubes in the Oracle Communications Data Model are cube materialized views (that is, CB$ objects).

Note:

immediately after installation, all materialized views underlying the OLAP cubes are disabled by default. To enable the cube materialized views, you must follow the steps outlined in "Adding Materialized View Capabilities".

For information on the using OLAP cubes in your customized version of Oracle Communications Data Model, see Oracle OLAP User's Guide and the following topics:

- Defining New Oracle OLAP Cubes for Oracle Communications Data Model

- Changing an Oracle OLAP Cube in Oracle Communications Data Model

- Creating a New Forecast Cube in Oracle Communications Data Model

- Choosing a Cube Partitioning Strategy for Oracle Communications Data Model

- Choosing a Cube Data Maintenance Method for Oracle Communications Data Model

Defining New Oracle OLAP Cubes for Oracle Communications Data Model

You can add new OLAP cubes to the ocdm_sys schema. For consistency's sake, design and define these new cubes as described in "Characteristics of the OLAP Cubes in Oracle Communications Data Model".

Take the following steps to define new cubes:

1. Ensure that there is an aggregate table (DWA_) to use as the "lowest leaf" data for the cube. See "Aggregate Tables in the Oracle Communications Data Model" for information on creating new tables.

2. Use the AWM to define new Cubes for a customized version of Oracle Communications Data Model. Follow the instructions given for creating cubes and dimensions in Oracle OLAP User's Guide.
Use the information provided in "Characteristics of the OLAP Dimensional Model". and the Oracle OLAP User's Guide to guide you when you design and define new OLAP cubes. Also, if you are familiar with a relational star schema design as outlined in "Characteristics of Relational Star and Snowflake Tables", then you can use this understanding to help you design an OLAP Cube as described below:

- Fact tables correspond to cubes.

- Data columns in the fact tables correspond to measures.

- Foreign key constraints in the fact tables identify the dimension tables.

- Dimension tables identify the dimensions.

- Primary keys in the dimension tables identify the base-level dimension members.

- Parent columns in the dimension tables identify the higher level dimension members.

- Columns in the dimension tables containing descriptions and characteristics of the dimension members identify the attributes.

You can also get insights into the dimensional model by looking at the sample reports included with Oracle Communications Data Model.

See:

Oracle Communications Data Model Installation Guide for more information on installing the sample reports and deploying the Oracle Communications Data Model RPD and webcat on the Business Intelligence Suite Enterprise Edition instance.

Tip:

While investigating your source data, you may decide to create relational views that more closely match the dimensional model that you plan to create.

3. Add materialized view capabilities to the OLAP cubes as described in "Adding Materialized View Capabilities".

See also:

Oracle OLAP User's Guide, "Defining New Oracle OLAP Cubes for Oracle Communications Data Model", and the sample reports in Oracle Communications Data Model Reference.

Oracle by Example:

For more information on creating OLAP cubes, see the "Building OLAP 11g Cubes" OBE tutorial.
To access the tutorial, open the Oracle Learning Library in your browser by following the instructions in "Oracle Technology Network"; and, then, search for the tutorial by name.

Changing an Oracle OLAP Cube in Oracle Communications Data Model

Common customizations to Oracle Communications Data Model cubes are changing the dimensions or the measures of the cube.

Since all Oracle Communications Data Model cubes load data from tables with the DWA_ prefix, to change the measures or dimensions of one cube, you must take the following steps:

1. Use the information in Oracle Communications Data Model Reference, to identify the DWA_ table from which the OLAP cube is populated.

2. Change the structure of the DWA_ table identified in Step 1.

3. Change the OLAP cube and cube materialized views to reflect the new structure.

Creating a New Forecast Cube in Oracle Communications Data Model

To create a new forecast in Oracle Communications Data Model:

1. Create a cube to contain the results of the forecast as described in "Defining New Oracle OLAP Cubes for Oracle Communications Data Model".

Note:

You cannot enable materialized views for an Oracle Communications Data Model forecast cube.

2. Write an OLAP DML forecasting context program as described in Oracle OLAP DML Reference.

Choosing a Cube Partitioning Strategy for Oracle Communications Data Model

Partitioning is a method of physically storing the contents of a cube. It improves the performance of large cubes in the following ways:

- Improves scalability by keeping data structures small. Each partition functions like a smaller measure.

- Keeps the working set of data smaller both for queries and maintenance, since the relevant data is stored together.

- Enables parallel aggregation during data maintenance. Each partition can be aggregated by a separate process.

- Simplifies removal of old data from storage. Old partitions can be dropped, and new partitions can be added.

The number of partitions affects the database resources that can be allocated to loading and aggregating the data in a cube. Partitions can be aggregated simultaneously when sufficient resources have been allocated.

The Cube Partitioning Advisor analyzes the source tables and develops a partitioning strategy. You can accept the recommendations of the Cube Partitioning Advisor, or you can make your own decisions about partitioning.

If your partitioning strategy is driven primarily by life-cycle management considerations, then you should partition the cube on the Time dimension. Old time periods can then be dropped as a unit, and new time periods added as a new partition. The Cube Partitioning Advisor has a Time option, which recommends a hierarchy and a level in the Time dimension for partitioning.

The level on which to partition a cube is determined based on a trade off between load performance and query performance.

Typically, you do not want to partition on too low a level (for example, on the DAY level of a TIME dimension) because if you do then too many partitions must be defined at load time which slows down an initial or historical load. Also, a large number of partitions can result in unusually long Analytic Workspace attach times and slows down the Time Series-based calculations. Also, a Quarterly Cumulative measure (Quarter to Date Measure) needs to access 90 or 91 partitions to calculate a specific value for one Customer and Organization. All dimension members above the partition level of partition dimension (including those belonging to nondefault hierarchies) would be present in a single default template. Day level partitioning makes this very heavy since all higher level members are stored in default template. However, the advantage of partitioning DAY if the OLAP Cube load frequency is daily then there you must only load from a new partition in fact table into a single partition in the OLAP cube every day. This greatly improves the load performance since percentage-based refresh can be enabled if the cube is materialized-view enabled and has materialized-view logs.

Recommendation

Usually a good compromise between the differing load and query performance requirements is to use an intermediate level like MONTH as the partition level. Time series calculations within a month (week to date, month to date, and so on) are fast and higher level calculation like year to date needs to refer to 12 partitions at most. Also this way the monthly partition is defined and created only one time (that is during the initial load on first of each month) and is then reused for each subsequent load that month. The aggregation process may be triggered off at the month level (instead of specific day level) and some redundant aggregations (of previously loaded dates of current month) may occur each time but it should result in satisfactory load and query performance.

See also:

"The discussion on choosing a partition strategy in Oracle OLAP User's Guide, "Indexes and Partitioned Indexes in the Oracle Communications Data Model", and "Partitioning and Materialized Views".

Choosing a Cube Data Maintenance Method for Oracle Communications Data Model

While developing a dimensional model of your data, it is a good idea to map and load each object immediately after you create it so that you can immediately detect and correct any errors that you made to the object definition or the mapping.

However, in a production environment, you want to perform routine maintenance as quickly and easily as possible. For this stage, you can choose among data maintenance methods. You can refresh all cubes using the Maintenance Wizard. This wizard enables you to refresh a cube immediately, or submit the refresh as a job to the Oracle job queue, or generate a PL/SQL script. You can run the script manually or using a scheduling utility, such as Oracle Enterprise Manager Scheduler or the DBMS_SCHEDULER PL/SQL package. The generated script calls the BUILD procedure of the DBMS_CUBE PL/SQL package. You can modify this script or develop one from the start using this package.

The data for a partitioned cube is loaded and aggregated in parallel when multiple processes have been allocated to the build. You are able to see this in the build log.

In addition, each cube can support these data maintenance methods:

- Custom cube scripts

- Cube materialized views

If you are defining cubes to replace existing materialized views, then you use the materialized views as an integral part of data maintenance. Note, however, that materialized view capabilities restrict the types of analytics that can be performed by a custom cube script.

See also:

Oracle OLAP User's Guide and "Types of Materialized Views and Refresh options"

Oracle by Example:

See the following OBE tutorial for an example of how Oracle uses cube materialized views for transparent access to a relational star schema.:

- "Querying OLAP 11g Cubes"

To access the tutorial, open the Oracle Learning Library in your browser by following the instructions in "Oracle Technology Network"; and, then, search for the tutorial by name.

Materialized Views in the Oracle Communications Data Model

Materialized views are query results that have been stored or "materialized" in advance as schema objects. From a physical design point of view, materialized views resemble tables or partitioned tables and behave like indexes in that they are used transparently and improve performance.

In the past, organizations using summaries spent a significant amount of time and effort creating summaries manually, identifying which summaries to create, indexing the summaries, updating them, and advising their users on which ones to use. With the advent of materialized views, a database administrator creates one or more materialized views, which are the equivalent of a summary. Thus, the workload of the database administrator is eased and the user no longer needed to be aware of the summaries that had been defined. Instead, the end user queries the tables and views at the detail data level. The query rewrite mechanism in the Oracle server automatically rewrites the SQL query to use the summary tables and reduces response time for returning results from the query.

Materialized views improve query performance by precalculating expensive join and aggregation operations on the database before executing and storing the results in the database. The query optimizer automatically recognizes when an existing materialized view can and should be used to satisfy a request.

The default Oracle Communications Data Model defines many materialized views. In the default ocdm_sys schema, you can identify these materialized views by looking at objects with the prefixes listed in the following table.

Prefix

Description

DWA_

Aggregate tables which are materialized views.
See: Aggregate tables in Oracle Communications Data Model Reference for a list of these objects in the default data model.

"Aggregate Tables in the Oracle Communications Data Model" for more information on customizing these objects,.

CB$

An OLAP cube enhanced with materialized view capabilities.
See: OLAP cube materialized views in Oracle Communications Data Model Reference for a list of these objects in the default data model.

"Characteristics of the OLAP Cubes in Oracle Communications Data Model" for information on OLAP cubes.

Note: Do not report or query against this object. Instead access the relational view of an OLAP cube (that is, the object with the _VIEW suffix).

DMV_

Materialized views created for performance reasons (that is, not an aggregate table or a cube materialized view).
See: Oracle Communications Data Model Reference to identify these objects in the default data model.

The following topics provide more information on using and creating materialized views in your customized Oracle Communications Data Model:

- Types of Materialized Views and Refresh options

- Choosing Indexes for Materialized Views

- Partitioning and Materialized Views

- Compressing Materialized Views

Types of Materialized Views and Refresh options

Refresh option vary by the type of materialized view:

- Refresh Options for Materialized Views with Aggregates

- Refresh Options for Materialized Views Containing Only Joins

- Refresh Options for Nested Materialized Views

See:

Oracle OLAP User's Guide for a discussion of creating materialized views of Oracle OLAP cubes.

Refresh Options for Materialized Views with Aggregates

In data warehouses, materialized views normally contain aggregates. The _DWA tables in the default Oracle Communications Data Model are this type of materialized view.

For a materialized view with aggregates, for fast refresh to be possible:

- The SELECT list must contain all of the GR OUP BY columns (if present)

- There must be a COUNT(*) and a COUNT(column) on any aggregated columns.

- Materialized view logs must be present on all tables referenced in the query that defines the materialized view. The valid aggregate functions are: SUM, COUNT(x), COUNT(*), AVG, VARIANCE, STDDEV, MIN, and MAX, and the expression to be aggregated can be any SQL value expression.

Fast refresh for a materialized view containing joins and aggregates is possible after any type of DML to the base tables (direct load or conventional INSERT, UPDATE, or DELETE).

You can define that the materialized view be refreshed ON COMMIT or ON DEMAND. A REFRESH ON COMMIT materialized view is automatically refreshed when a transaction that does DML to a materialized view's detail tables commits.

When you specify REFRESH ON COMMIT, the table commit can take more time than if you have not. This is because the refresh operation is performed as part of the commit process. Therefore, this method may not be suitable if many users are concurrently changing the tables upon which the materialized view is based.

Refresh Options for Materialized Views Containing Only Joins

Some materialized views contain only joins and no aggregates (for example, when a materialized view is created that joins the sales table to the times and customers tables). The advantage of creating this type of materialized view is that expensive joins are precalculated.

Fast refresh for a materialized view containing only joins is possible after any type of DML to the base tables (direct-path or conventional INSERT, UPDATE, or DELETE).

A materialized view containing only joins can be defined to be refreshed ON COMMIT or ON DEMAND. If it is ON COMMIT, the refresh is performed at commit time of the transaction that does DML on the materialized view's detail table.

If you specify REFRESH FAST, Oracle performs further verification of the query definition to ensure that fast refresh can be performed if any of the detail tables change. These additional checks are:

- A materialized view log must be present for each detail table unless the table supports partition change tracking. Also, when a materialized view log is required, the ROWID column must be present in each materialized view log.

- The rowids of all the detail tables must appear in the SELECT list of the materialized view query definition.

If some of these restrictions are not met, you can create the materialized view as REFRESH FORCE to take advantage of fast refresh when it is possible. If one table does not meet all of the criteria, but the other tables do the materialized view is still fast refreshable with respect to the other tables for which all the criteria are met.

To achieve an optimally efficient refresh:

- Ensure that the defining query does not use an outer join that behaves like an inner join. If the defining query contains such a join, consider rewriting the defining query to contain an inner join.

- If the materialized view contains only joins, the ROWID columns for each table (and each instance of a table that occurs multiple times in the FROM list) must be present in the SELECT list of the materialized view.

- If the materialized view has remote tables in the FROM clause, all tables in the FROM clause must be located on that same site. Further, ON COMMIT refresh is not supported for materialized view with remote tables. Except for SCN-based materialized view logs, materialized view logs must be present on the remote site for each detail table of the materialized view and ROWID columns must be present in the SELECT list of the materialized view.

Refresh Options for Nested Materialized Views

A nested materialized view is a materialized view whose definition is based on another materialized view. A nested materialized view can reference other relations in the database in addition to referencing materialized views.

In a data warehouse, you typically create many aggregate views on a single join (for example, rollups along different dimensions). Incrementally maintaining these distinct materialized aggregate views can take a long time, because the underlying join has to be performed many times.

Using nested materialized views, you can create multiple single-table materialized views based on a joins-only materialized view and the join is performed just one time. In addition, optimizations can be performed for this class of single-table aggregate materialized view and thus refresh is very efficient.

Some types of nested materialized views cannot be fast refreshed. Use EXPLAIN_MVIEW to identify those types of materialized views.

You can refresh a tree of nested materialized views in the appropriate dependency order by specifying the nested =TRUE parameter with the DBMS_MVIEW.REFRESH parameter. For example, if you call DBMS_MVIEW.REFRESH ('SUM_SALES_CUST_TIME', nested => TRUE), the REFRESH procedure first refreshes the join_sales_cust_time materialized view, and then refreshes the sum_sales_cust_time materialized view.

Choosing Indexes for Materialized Views

The two most common operations on a materialized view are query execution and fast refresh, and each operation has different performance requirements:

- Query execution might need to access any subset of the materialized view key columns, and might need to join and aggregate over a subset of those columns. Consequently, for best performance, create a single-column bitmap index on each materialized view key column.

- In the case of materialized views containing only joins using fast refresh, create indexes on the columns that contain the rowids to improve the performance of the refresh operation.

- If a materialized view using aggregates is fast refreshable, then an index appropriate for the fast refresh procedure is created unless USING NO INDEX is specified in the CREATE MATERIALIZED VIEW statement.

See also:

"Indexes and Partitioned Indexes in the Oracle Communications Data Model"

Partitioning and Materialized Views

Because of the large volume of data held in a data warehouse, partitioning is an extremely useful option when designing a database. Partitioning the fact tables improves scalability, simplifies system administration, and makes it possible to define local indexes that can be efficiently rebuilt. Partitioning the fact tables also improves the opportunity of fast refreshing the materialized view because this may enable partition change tracking refresh on the materialized view.

Partitioning a materialized view has the same benefits as partitioning fact tables. When a materialized view is partitioned a refresh procedure can use parallel DML in more scenarios and partition change tracking-based refresh can use truncate partition to efficiently maintain the materialized view.

See also:

Oracle Database VLDB and Partitioning Guide, "Partitioned Tables in the Oracle Communications Data Model", "Indexes and Partitioned Indexes in the Oracle Communications Data Model", and "Choosing a Cube Partitioning Strategy for Oracle Communications Data Model"

Using Partition Change Tracking

It is possible and advantageous to track freshness to a finer grain than the entire materialized view. The ability to identify which rows in a materialized view are affected by a certain detail table partition, is known as partition change tracking. When one or more of the detail tables are partitioned, it may be possible to identify the specific rows in the materialized view that correspond to a modified detail partition(s). those rows become stale when a partition is modified while all other rows remain fresh.

You can use partition change tracking to identify which materialized view rows correspond to a particular partition. Partition change tracking is also used to support fast refresh after partition maintenance operations on detail tables. For instance, if a detail table partition is truncated or dropped, the affected rows in the materialized view are identified and deleted. Identifying which materialized view rows are fresh or stale, rather than considering the entire materialized view as stale, allows query rewrite to use those rows that refresh while in QUERY_REWRITE_INTEGRITY = ENFORCED or TRUSTED modes.

Several views, such as DBA_MVIEW_DETAIL_PARTITION, detail which partitions are stale or fresh. Oracle does not rewrite against partial stale materialized views if partition change tracking on the changed table is enabled by the presence of join dependent expression in the materialized view.

To support partition change tracking, a materialized view must satisfy the following requirements:

- At least one detail table referenced by the materialized view must be partitioned.

- Partitioned tables must use either range, list or composite partitioning.

- The top level partition key must consist of only a single column.

- The materialized view must contain either the partition key column or a partition marker or ROWID or join dependent expression of the detail table.

- If you use a GROUP BY clause, the partition key column or the partition marker or ROWID or join dependent expression must be present in the GROUP BY clause.

- If you use an analytic window function or the MODEL clause, the partition key column or the partition marker or ROWID or join dependent expression must be present in their respective PARTITION BY subclauses.

- Data modifications can only occur on the partitioned table. If partition change tracking refresh is being done for a table which has join dependent expression in the materialized view, then data modifications should not have occurred in any of the join dependent tables.

- The COMPATIBILITY initialization parameter must be a minimum of 9.0.0.0.0.

- Partition change tracking is not supported for a materialized view that refers to views, remote tables, or outer joins.

Compressing Materialized Views

Using data compression for a materialized view brings you a additional dramatic performance improvement.

Consider data compression when using highly redundant data, such as tables with many foreign keys. In particular, likely candidates are materialized views created with the ROLLUP clause.

See also:

"Data Compression in the Oracle Communications Data Model", "Exadata Hybrid Columnar Compression", and "Aggregate Tables in the Oracle Communications Data Model".

5 ETL Implementation and Customization

This chapter discusses the ETL (extraction, transformation and loading) programs you use to populate an Oracle Communications Data Model warehouse. It includes the following topics:

- The Role of ETL in the Oracle Communications Data Model

- Writing Source-ETL for Oracle Communications Data Model

- Customizing Intra-ETL for the Oracle Communications Data Model

The Role of ETL in the Oracle Communications Data Model

Figure 3-1, "Layers of an Oracle Communications Data Model Warehouse" illustrated the three layers in Oracle Communications Data Model warehouse environment: the optional staging layer, the foundation layer, and the access layer. As illustrated by Figure 5-1, you use two types of ETL (extraction, transformation and loading) to populate these layers:

- Source-ETL. ETL that populates the staging layer (if any) and the foundation layer with data from the OLTP system is known as source ETL.
Oracle Communications Data Model does not include source-ETL. You must write source-ETL yourself using your understanding of your OLTP system and your customized Oracle Communications Data Model. See "Writing Source-ETL for Oracle Communications Data Model" for more information on writing source-ETL.

- Intra-ETL. ETL that populates the access layer using the data in the foundation layer is known as intra-ETL.
Oracle Communications Data Model does include intra-ETL. You can modify the default intra-ETL to populate a customized access layer from a customized foundation layer. See "Customizing Intra-ETL for the Oracle Communications Data Model" for more information on the intra-ETL.

Figure 5-1 ETL Flow Diagram for Oracle Communications Data Model

Writing Source-ETL for Oracle Communications Data Model

ETL that populates the staging layer (if any) and the foundation layer of an Oracle Communications Data Model warehouse with data from the OLTP system is known as source-ETL. Because each OLTP system is unique, source-ETL is not provided with Oracle Communications Data Model. You must write source-ETL yourself.

Keep the following points in mind when designing and writing source-ETL for Oracle Communications Data Model:

- You can populate the calendar data by using the calendar population scripts provided with Oracle Communications Data Model and described in Oracle Communications Data Model Reference.

- Populate the tables in the following order:

1. Lookup tables

2. Reference tables

3. Base tables

- Analyze the tables in one category before loading the tables in the next category (for example, analyze the reference tables before loading the lookup tables). Additionally, you must analyze all of the tables loaded by the source-ETL process before executing the intra-ETL processes).

See:

The topic about analyzing tables, indexes and clusters in Oracle Database Administrator's Guide.

You can use Oracle Warehouse Builder to write source-ETL or you can write your own using another ETL tool or mapping scripts.

See also:

Oracle Warehouse Builder Data Modeling, ETL, and Data Quality Guide.

Oracle By Example:

See the following OBE tutorials for more information on Oracle Warehouse Builder:

- "Setting Up the Oracle Warehouse Builder 11g Release 2 Environment"

- "Improved User Interface, Usability, and Productivity With Oracle Warehouse Builder 11g R2"

- "Using Data Transformation Operators with Source and Target Operators"

- "Working with Pluggable Mappings"

- "Examining Source Data Using Data Profiling with Database 11g Release 2"

To access the tutorials, open the Oracle Learning Library in your browser by following the instructions in "Oracle Technology Network"; and, then, search for the tutorials by name.

The following topics provide general information about writing source-ETL:

- ETL Architecture for Oracle Communications Data Model Source-ETL

- Creating a Source to Target Mapping Document for the Source-ETL

- Designing a Plan for Rectifying Source-ETL Data Quality Problems

- Designing Source-ETL Workflow and Jobs Control

- Designing Source-ETL Exception Handling

- Writing Source-ETL that Loads Efficiently

ETL Architecture for Oracle Communications Data Model Source-ETL

ETL first extracts data from the original sources, assures the quality of the data, cleans the data, and makes the data consistent across the original sources. ETL then populates the physical objects with the "clean" data so that query tools, report writers, dashboards and so on can access the data.

The fundamental services upon which data acquisition is constructed are as follows:

- Data sourcing

- Data movement

- Data transformation

- Data loading

From a logical architecture perspective, there are many different ways to configure these building blocks for delivering data acquisition services. The major architectural styles available that cover a range of options to be targeted within a data warehousing architecture include:

- Batch Extract, Transform, and Load and Batch Extract, Load, Transform, Load
Batch Extract, Transform and Load (ETL) and Batch Extract, Load, Transform, Load (ELTL) are the traditional architecture sin data warehouse implementation. The difference between them is where the transformation proceed in or out database.

- Batch Hybrid Extract, Transform, Load, Transform, Load
Batch Hybrid Extract, Transform, Load, Transform, Load (ETLTL) is a hybrid strategy. This strategy provides the most flexibility to remove hand coding approaches to transformation design, apply a metadata-driven approach, and still be able to leverage the data processing capabilities of the enterprise warehouse. In this targeted design, the transformation processing is first performed outside the warehouse as a pre-processing step before loading the staging tables, and then further transformation processing is performed within the data warehouse before the final load into the target tables.

- Real-time Extract, Transform, Load
Real-time Extract, Transform, Load (rETL) is appropriate when service levels for data freshness demand more up-to-date information in the data warehousing environment. In this approach, the OLTP system must actively publish events of interest so that the rETL processes can extract them from a message bus (queue) on a timely basis. A message-based paradigm is used with publish and subscribe message bus structures or point-to-point messaging with reliable queues.

When designing source-ETL for Oracle Communications Data Model, use the architecture that best meets your business needs.

Creating a Source to Target Mapping Document for the Source-ETL

Before you begin building your extract systems, create a logical data interface document that maps the relationship between original source fields and target destination fields in the tables. This document ties the very beginning of the ETL system to the very end.

Columns in the data mapping document are sometimes combined. For example, the source database, table name, and column name could be combined into a single target column. The information within the concatenated column would be delimited with a period. Regardless of the format, the content of the logical data mapping document has been proven to be the critical element required to sufficiently plan ETL processes.

Designing a Plan for Rectifying Source-ETL Data Quality Problems

Data cleaning consists of all the steps required to clean and validate the data feeding a table and to apply known business rules to make the data consistent. The perspectives of the cleaning and conforming steps are less about the upside potential of the data and more about containment and control.

If there are data quality problems, then build a plan, in agreement with IT and business users, for how to rectify these problems.

Answer the following questions:

- Is data missing?

- Is the data wrong or inconsistent?

- Should the problem be fixed in the source systems?

- Set up the data quality reporting and action program and people responsibility.

Set up the following processes and programs:

- Set up a data quality measurement process.

- Set up the data quality reporting and action program and people responsibility.

Designing Source-ETL Workflow and Jobs Control

All data movement among ETL processes are composed of jobs. An ETL workflow executes these jobs in the proper sequence and with regard to the necessary dependencies. General ETL tools, such as Oracle Warehouse Builder, support this kind of workflow, job design, and execution control.

Below are some tips when you design ETL jobs and workflow:

- Use common structure across all jobs (source system to transformer to target data warehouse).

- Have a one-to-one mapping from source to target.

- Define one job per Source table.

- Apply generic job structure and template jobs to allow for rapid development and consistency.

- Use an optimized job design to leverage Oracle load performance based on data volumes.

- Design parameterized job to allow for greater control over job performance and behavior.

- Maximize Jobs parallelism execution.

Designing Source-ETL Exception Handling

Your ETL tool or your developed mapping scripts generate status and error handling tables.

As a general principle, all ETL logs status and errors into a table. You monitor execution status using an ETL tool or by querying this log table directly.

Writing Source-ETL that Loads Efficiently

Whether you are developing mapping scripts and loading into a staging layer or directly into the foundation layer the goal is to get the data into the warehouse in the most expedient manner. In order to achieve good performance during the load you must begin by focusing on where the data to be loaded resides and how you load it into the database. For example, you should not use a serial database link or a single JDBC connection to move large volumes of data. The most common and preferred mechanism for loading large volumes of data is loading from flat files.

The following topics discuss best practices for ensuring your source-ETL loads efficiently:

- Using a Staging Area for Flat Files

- Preparing Raw Data Files for Source-ETL

- Source-ETL Data Loading Options

- Parallel Direct Path Load Source-ETL

- Partition Exchange Load for Oracle Communications Data Model Source-ETL

Using a Staging Area for Flat Files

The area where flat files are stored before being loaded into the staging layer of a data warehouse system is commonly known as staging area. The overall speed of your load is determined by:

- How quickly the raw data can be read from staging area.

- How quickly the raw data can be processed and inserted into the database.

Recommendations

Stage the raw data across as many physical disks as possible to ensure that reading it is not a bottleneck during the load.

Also, if you are using the Exadata Database Machine, the best place to stage the data is in an Oracle Database File System (DBFS) stored on the Exadata storage cells. DBFS creates a mountable cluster file system which can you can use to access files stored in the database. Create the DBFS in a separate database on the Database Machine. This allows the DBFS to be managed and maintained separately from the data warehouse.

Mount the file system using the DIRECT_IO option to avoid thrashing the system page cache while moving the raw data files in and out of the file system.

See:

Oracle Database SecureFiles and Large Objects Developer's Guide for more information on setting up DBFS, and Appendix A, "Exadata and the Oracle Communications Data Model."

Preparing Raw Data Files for Source-ETL

In order to parallelize the data load Oracle Database must be able to logically break up the raw data files into chunks, known as granules. To ensure balanced parallel processing, the number of granules is typically much higher than the number of parallel server processes. At any given point in time, a parallel server process is allocated one granule to work on. After a parallel server process completes working on its granule, another granule is allocated until all of the granules are processed and the data is loaded.

Recommendations

Follow these recommendations:

- Deliminate each row using a known character such as a new line or a semicolon. This ensures that Oracle can look inside the raw data file and determine where each row of data begins and ends in order to create multiple granules within a single file.

- If a file is not position-able and seek-able (for example the file is compressed or zip file), then the files cannot be broken up into granules and the whole file is treated as a single granule. In this case, only one parallel server process can work on the entire file. In order to parallelize the loading of compressed data files, use multiple compressed data files. The number of compressed data files used determines the maximum parallel degree used by the load.

- When loading multiple data files (compressed or uncompressed):

- Use a single external table, if at all possible

- Make the files similar in size

- Make the size of the files a multiple of 10 MB

- If you must have files of different sizes, list the files from largest to smallest. By default, Oracle assumes that the flat file has the same character set as the database. If this is not the case, specify the character set of the flat file in the external table definition to ensure the proper character set conversions can take place.

Source-ETL Data Loading Options

Oracle offers several data loading options

- External table or SQL*Loader

- Oracle Data Pump (import and export)

- Change Data Capture and Trickle feed mechanisms (such as Oracle GoldenGate)

- Oracle Database Gateways to open systems and mainframes

- Generic Connectivity (ODBC and JDBC)

The approach that you take depends on the source and format of the data you receive.

Recommendations for Loading Flat Files

If you are loading from files into Oracle you have two options: SQL*Loader or external tables.

Using external tables offers the following advantages:

- Allows transparent parallelization inside the database.You can avoid staging data and apply transformations directly on the file data using arbitrary SQL or PL/SQL constructs when accessing external tables. SQL Loader requires you to load the data as-is into the database first.

- Parallelizing loads with external tables enables a more efficient space management compared to SQL*Loader, where each individual parallel loader is an independent database sessions with its own transaction. For highly partitioned tables this could potentially lead to a lot of wasted space.

You can create an external table using the standard CREATE TABLE statement. However, to load from flat files the statement must include information about where the flat files reside outside the database. The most common approach when loading data from an external table is to issue a CREATE TABLE AS SELECT (CTAS) statement or an INSERT AS SELECT (IAS) statement into an existing table.

Parallel Direct Path Load Source-ETL

A direct path load parses the input data according to the description given in the external table definition, converts the data for each input field to its corresponding Oracle data type, then builds a column array structure for the data. These column array structures are used to format Oracle data blocks and build index keys. The newly formatted database blocks are then written directly to the database, bypassing the standard SQL processing engine and the database buffer cache.

The key to good load performance is to use direct path loads wherever possible:

- A CREATE TABLE AS SELECT (CTAS) statement always uses direct path load.

- A simple INSERT AS SELECT (IAS) statement does not use direct path load. In order to achieve direct path load with an IAS statement you must add the APPEND hint to the command.

Direct path loads can also run in parallel. To set the parallel degree for a direct path load, either:

- Add the PARALLEL hint to the CTAS statement or an IAS statement.

- Set the PARALLEL clause on both the external table and the table into which the data is loaded.
After the parallel degree is set:

- A CTAS statement automatically performs a direct path load in parallel.

- An IAS statement does not automatically perform a direct path load in parallel. In order to enable an IAS statement to perform direct path load in parallel, you must alter the session to enable parallel DML by executing the following statement.

alter session enable parallel DML;

Partition Exchange Load for Oracle Communications Data Model Source-ETL

A benefit of partitioning is the ability to load data quickly and easily with minimal impact on the business users by using the EXCHANGE PARTITION command. The EXCHANGE PARTITION command enables swapping the data in a nonpartitioned table into a particular partition in your partitioned table. The EXCHANGE PARTITION command does not physically move data, instead it updates the data dictionary to exchange a pointer from the partition to the table and vice versa.

Because there is no physical movement of data, an exchange does not generate redo and undo. In other words, an exchange is a sub-second operation and far less likely to impact performance than any traditional data-movement approaches such as INSERT.

Recommendations

Partition the larger tables and fact tables in the data warehouse.

Example 5-1 Using Exchange Partition Statement with a Partitioned Table for Quick Exchange of Data

Assume that there is a large table called Sales, which is range partitioned by day. At the end of each business day, data from the online sales system is loaded into the Sales table in the warehouse.

The following steps ensure the daily data gets loaded into the correct partition with minimal impact to the business users of the data warehouse and optimal speed:

1. Create external table for the flat file data coming from the online system

2. Using a CTAS statement, create a nonpartitioned table called tmp_sales that has the same column structure as Sales table

3. Build any indexes that are on the Sales table on the tmp_sales table

4. Issue the EXCHANGE PARTITION command.

Alter table Sales exchange partition p2 with
table top_sales including indexes without validation;

5. Gather optimizer statistics on the newly exchanged partition using incremental statistics.

The EXCHANGE PARTITION command in this example, swaps the definitions of the named partition and the tmp_sales table, so the data instantaneously exists in the right place in the partitioned table. Moreover, with the inclusion of the INCLUDING INDEXES and WITHOUT VALIDATION clauses, Oracle swaps index definitions and does not check whether the data actually belongs in the partition - so the exchange is very quick.

Note:

The assumption being made in this example is that the data integrity was verified at date extraction time. If you are unsure about the data integrity, omit the WITHOUT VALIDATION clause so that the Database checks the validity of the data.

Customizing Intra-ETL for the Oracle Communications Data Model

The Oracle Communications Data Model supports the use of ETL tools such as Oracle Warehouse Builder to define the workflow to execute the intra-ETL process. You can, of course, write your own Intra_ETL. However, an intra-ETL component is delivered with Oracle Communications Data Model that is a process flow designed using the Oracle Warehouse Builder Workflow component. This process flow is named INTRA_ETL_FLW.

As illustrated by Figure 5-1, "ETL Flow Diagram for Oracle Communications Data Model", the INTRA_ETL_FLW process flow uses the data in the Oracle Communications Data Model base, reference, and lookup tables to populate all of the other Oracle Communications Data Model structures. Within this package the dependency of each individual program is implemented and enforced so that each program executes in the proper order.

You can change the original intra-ETL script for your specific requirements. However, perform a complete impact analysis before you make the change. Package the changes as a patch to the original Oracle Communications Data Model intra-ETL mapping.

The following topics provide more information about the intra-ETL provided with Oracle Communications Data Model:

- Understanding the INTRA_ETL_FLW Process Flow

- Performing an Initial Load of an Oracle Communications Data Model Warehouse

- Managing Errors During Oracle Communications Data Model Intra-ETL Execution

Understanding the INTRA_ETL_FLW Process Flow

The INTRA_ETL_FLW process flow consists of the following subprocesses and includes the dependency of individual sub-process flows and executes them in the proper order:

1. DRVD_FLW
This sub-process flow contains all the Oracle Warehouse Builder mappings for populating derived tables based on the content of the base, reference, and lookup tables. This process flow allows mappings to be executed concurrently.

This sub-process uses the following technologies:

- MERGE clause. This statement is used for initial load and for incremental load. It determines if the data to be inserted or updated into target table based on checking key columns with incoming data and provides a much superior performance compared to an INSERT and UPDATE combination.

- TABLE function. This function is used mainly for derived tables (and some source data) in order to avoid needing a separate staging table. The absence of a staging table provides a performance boost in the ETL operation and simplifies database maintenance

- Pipeline clause. This clause is used in the TABLE function for incrementally returning the result set for further processing as soon as they are created. Therefore, the use of this clause avoids bulk return which makes the complete process much faster than using a normal return.

2. AGGR_FLW
This sub-process flow contains PL/SQL code that uses the partitions change tracking strategy to refresh all the aggregate tables which are materialized views in the Oracle Communications Data Model.

This subprocess uses the following technologies:

- Materialized View. A materialized view is used to hold the aggregation data. Whenever this is refreshed then the modified data get reflected in the corresponding aggregate table and this leads to a significant increase in the query execution performance. Moreover usage of materialized view allows Oracle Communications Data Model to make use of the Oracle Query Rewrite feature for better SQL optimization and hence improved performance.

- FAST Refresh. This refresh type is used to refresh the aggregates with only the incremental data (inserted and modified) in base and derived tables after the immediately previous refresh and this incremental refresh leads to much better performance and hence shorter intra-ETL window.

3. MINING_FLW
This sub-process flow triggers the data mining model refresh.

The INTRA_ETL_FLW process flow also includes the OLAP_MAP mapping. OLAP_MAP invokes the analytic workspace build function of the PKG_OCDM_OLAP_ETL_AW_LOAD package to load data from Oracle Communications Data Model aggregate materialized views into the Oracle Communications Data Model analytical workspace and calculates the forecast data. The PKG_OCDM_OLAP_ETL_AW_LOAD package reads OLAP ETL parameters from the DWC_OLAP_ETL_PARAMETER control table in the ocdm_sys schema to determine the specifics of how to load the data and perform the forecasts.

Note:

The shell script ocdm_execute_wf.sh delivered with Oracle Communications Data Model performs the same function as Oracle Warehouse Builder Workflow INTRA_ETL_FLW. This shell script does not require Oracle Warehouse Builder workflow. For more information about working with this script, see "Manually Executing the OCDM_EXECUTE_WF.SH Script".

See:

Oracle Communications Data Model Reference for detailed information about the Oracle Communications Data Model intra-ETL.

Performing an Initial Load of an Oracle Communications Data Model Warehouse

Performing an initial load of an Oracle Communications Data Model is a multistep process:

1. Load the reference, lookup, and base tables Oracle Communications Data Model warehouse by executing the source-ETL that you have written using the guidelines given in "Writing Source-ETL for Oracle Communications Data Model".

2. Load the remaining structures in the Oracle Communications Data Model, by taking the following steps:

1. Update the parameters in DWC_ETL_PARAMETER control table in the ocdm_sys schema so that the ETL can use this information (that is, the beginning and end date of the ETL period) when loading the derived and aggregate tables and views.
For an initial load of an Oracle Communications Data Model warehouse, specify the values shown in the following table.

Columns

Value

PROCESS_NAME

'OCDM-INTRA-ETL'

FROM_DATE_ETL

The beginning date of the ETL period.

TO_DATE_ETL

The ending date of the ETL period.

See:

Oracle Communications Data Model Reference for more information on the DWC_ETL_PARAMETER control table.

2. Update the Oracle Communications Data Model OLAP ETL parameters in DWC_OLAP_ETL_PARAMETER control table in the ocdm_sys schema to specify the build method and other build characteristics so that the ETL can use this information when loading the OLAP cube data.
For an initial load of the analytic workspace, specify values following the guidelines in Table 5-1.

Table 5-1 Values of Oracle Communications Data Model OLAP ETL Parameters in the DWC_OLAP_ETL_PARAMETER Table for Initial Load

Column Name

Value

PROCESS_NAME

'OCDM-INTRA-ETL'

BUILD_METHOD

C which specifies a complete refresh which clears all dimension values before loading.

CUBENAME

One of the following values that specifies the cubes you want to build:

- ALL specifies a build of the cubes in the Oracle Communications Data Model analytic workspace.

- cubename[[|cubename]...] specifies one or more cubesto build.

MAXJOBQUEUES

A decimal value that specifies the number of parallel processes to allocate to this job. (Default value is 4.) The value that you specify varies depending on the setting of the JOB_QUEUE_PROCESSES database initialization parameter.

CALC_FCST

One of the following values depending on whether you want to calculate forecast cubes:

- Y specifies calculate forecast cubes.

- N specifies do not calculate forecast cubes.

NO_FCST_YRS

If the value for the CALC_FCST column is Y, specify a decimal value that specifies how many years forecast data you want to calculate; otherwise, specify NULL.

FCST_MTHD

If the value for the CALC_FCST column is Y, then specify AUTO; otherwise, specify NULL.

FCST_ST_YR

If the value for the CALC_FCST column is Y, then specify value specified as yyyy which is the "start business year" of a historical period; otherwise, specify NULL.

FCST_END_YR

If the value for the CALC_FCST column is Y, then specify value specified as yyyy which is the "end business year" of a historical period; otherwise, specify NULL.

OTHER1

Specify NULL.

OTHER2

Specify NULL.

3. Execute the intra-ETL in one of the ways described in "Executing the Default Oracle Communications Data Model Intra-ETL ".

See also:

Appendix C, "Refreshing the Data in an Oracle Communications Data Model Warehouse."

Executing the Default Oracle Communications Data Model Intra-ETL

You can execute the intra-ETL packages provided with Oracle Communications Data Model in the following ways:

- As a Workflow within Oracle Warehouse Builder as described in "Executing the INTRA_ETL_FLW Workflow from Oracle Warehouse Builder".

- Without using Oracle Warehouse Builder Workflow as described in "Manually Executing the OCDM_EXECUTE_WF.SH Script".

In either case, you can execute the intra-ETL explicitly or invoke its execution in some other program or process (for example, the source-ETL process after its successful execution) or through a predefined schedule (for example, using Oracle Job Scheduling feature and so on).

See:

"Monitoring the Execution of the Intra-ETL Process", "Recovering an Intra ETL Process", and "Troubleshooting Intra-ETL Performance".

Executing the INTRA_ETL_FLW Workflow from Oracle Warehouse Builder

You can execute the INTRA_ETL_FLW process from within Oracle Warehouse Builder after you deploy it.

To deploy the INTRA_ETL_FLW process flow, take the following steps:

1. Confirm that Oracle Warehouse Builder Workflow has been installed as described in Oracle Communications Data Model Installation Guide.

2. Within Oracle Warehouse Builder, go to the Control Center Manager.

3. Select OLAP_PFLW, then select AGR_PFLW, then select the main process flow INTRA_ETL_FLW.

4. Right-click INTRA_ETL_FLW and select set action.

- If this is the first deployment, set action to Create.

- If this is a later deployment, set action to Replace.

Deploy the process flow.

After the deployment finishes successfully, INTRA_ETL_FLW is ready to execute .

See:

Oracle Warehouse Builder Sources and Targets Guide for information about Oracle Warehouse Builder.

Manually Executing the OCDM_EXECUTE_WF.SH Script

Oracle Communications Data Model provides a script that you can use to populate the intra-ETL without using an Oracle Warehouse Builder Workflow. This shell script is named ocdm_execute_wf.sh .

The ocdm_execute_wf.sh script performs the same function as Oracle Warehouse Builder Workflow INTRA_ETL_FLW. The script can be invoked by another process such as source-ETL, or according to a predefined schedule such as Oracle Job Scheduling.

1. The ocdm_execute_wf.sh program prompts you to enter the environmental variables described in the following table.

Variable

Description

TSNAME

The tnsname of target database.

SYSTEM password

SYSTEM account password of target database.

ocdm_sys password

The password of the ocdm_sys account.

ORACLE HOME

Oracle Database home (that is, the full path without ending "/").

2. Reads the values from the DWC_ETL_PARAMETER and DWC_OLAP_ETL_PARAMETER control tables in the ocdm_sys schema before executing the mappings in the correct order

3. The result of each table loading are tracked in the DWC_INTRA_ETL_PROCESS and DWC_INTRA_ETL_ACTIVITY control tables as described in "Monitoring the Execution of the Intra-ETL Process".

Managing Errors During Oracle Communications Data Model Intra-ETL Execution

This topic discusses how you can identify and manage errors during intra-ETL execution. It contains the following topics:

- Monitoring the Execution of the Intra-ETL Process

- Recovering an Intra ETL Process

- Troubleshooting Intra-ETL Performance

Monitoring the Execution of the Intra-ETL Process

Two ocdm_sys schema control tables, DWC_INTRA_ETL_PROCESS and DWC_INTRA_ETL_ACTIVITY, monitor the execution of the intra-ETL process. These tables are documented in Oracle Communications Data Model Reference.

Each normal run (as opposed to an error-recovery run) of a separate intra-ETL execution performs the following steps:

1. Inserts a record into the DWC_INTRA_ETL_PROCESS table with a monotonically increasing system generated unique process key, SYSDATE as process start time, RUNNING as the process status, and an input date range in the FROM_DATE_ETL and TO_DATE_ETL columns.

2. Invokes each of the individual intra-ETL programs in the appropriate order of dependency. Before the invocation of each program, the procedure inserts a record into the intra-ETL Activity detail table, DWC_INTRA_ETL_ACTIVITY, with values for:

- ACTIVITY_KEY, a system generated unique activity key.

- PROCESS_KEY, the process key value corresponding to the intra-ETL process.

- ACTIVITY_NAME, an individual program name.

- ACTIVITY_DESC, a suitable activity description.

- ACTIVITY_START_TIME, the value of SYSDATE.

- ACTIVITY_STATUS, the value of RUNNING.

3. Updates the corresponding record in the DWC_INTRA_ETL_ACTIVITY table for the activity end time and activity status after the completion of each individual ETL program (either successfully or with errors). For successful completion of the activity, the procedure updates the status as 'COMPLETED-SUCCESS'. When an error occurs, the procedure updates the activity status as 'COMPLETED-ERROR', and also updates the corresponding error detail in the ERROR_DTL column.

4. Updates the record corresponding to the process in the DWC_INTRA_ETL_ PROCESS table for the process end time and status, after the completion of all individual intra-ETL programs. When all the individual programs succeed, the procedure updates the status to 'COMPLETED-SUCCESS', otherwise it updates the status to 'COMPLETED-ERROR'.

You can monitor the execution state of the intra-ETL, including current process progress, time taken by individual programs, or the complete process, by viewing the contents of the DWC_INTRA_ETL_PROCESS and DWC_INTRA_ETL_ACTIVITY tables corresponding to the maximum process key. Monitoring can be done both during and after the execution of the intra-ETL procedure.

Recovering an Intra ETL Process

To recover an intra-ETL process

1. Identify the errors by looking at the corresponding error details that are tracked against the individual programs in the DWC_INTRA_ETL_ACTIVITY table.

2. Correct the causes of the errors.

3. Re-invoke the intra-ETL process.

The INTRA_ETL_FLW process identifies whether it is a normal run or recovery run by referring the DWC_INTRA_ETL_ACTIVITY table. During a recovery run, INTRA_ETL_FLW executes only the necessary programs. For example, in the case of a derived population error as a part of the previous run, this recovery run executes the individual derived population programs which produced errors in the previous run. After their successful completion, the run executes the aggregate population programs and materialized view refresh in the appropriate order.

In this way, the intra-ETL error recovery is almost transparent, without involving the data warehouse or ETL administrator. The administrator must only correct the causes of the errors and re-invoke the intra-ETL process. The intra-ETL process identifies and executes the programs that generated errors.

Troubleshooting Intra-ETL Performance

To troubleshoot the performance of the intra-ETL:

- Check the execution plan as described in "Checking the Execution Plan".

- Monitor parallel DML executions as described in "Monitoring PARALLEL DML Executions".

- Check that data mining models were created correctly as described in "Troubleshooting Data Mining Model Creation".

Checking the Execution Plan

Use SQLDeveloper or other tools to view the package body of the code generated by Oracle Warehouse Builder.

For example, take the following steps to examine DWD_ACCT_PYMT_DAY__MAP:

1. Copy out the main query statement from code viewer.
Copy from "CURSOR "AGGREGATOR_c" IS …." to end of the query, which is right above another "CURSOR "AGGREGATOR_c$1" IS".

2. In SQLDeveloper worksheet, issue the following statement to turn on the parallel DML:

Alter session enable parallel dml;

3. Paste the main query statement into another SQL Developer worksheet and view the execution plan by clicking F6.
Carefully examine the execution plan to make the mapping runs according to a valid plan.

Monitoring PARALLEL DML Executions

Check that you are running mapping in parallel mode by executing the following SQL statement to count the executed "Parallel DML/Query" statement

column name format a50
column value format 999,999
SELECT NAME, VALUE
FROM GV$SYSSTAT
WHERE UPPER (NAME) LIKE '%PARALLEL OPERATIONS%'
OR UPPER (NAME) LIKE '%PARALLELIZED%'
OR UPPER (NAME) LIKE '%PX%'
;

If you run mapping in parallel mode, you should see "DML statements parallelized" increased by 1 (one) every time the mapping was invoked. If not, you do not see this increase, then the mapping was not invoked as "parallel DML".

If you see "queries parallelized" increased by 1 (one) instead, then typically it means that the SELECT statement inside of the INSERT was parallelized, but that INSERT itself was not parallelized.

See also:

"Parallel Execution in the Oracle Communications Data Model"

Troubleshooting Data Mining Model Creation

After the data mining models are created, check the error log in ocdm_sys.dwc_intra_etl_activity table. For example, execute the following code.

set line 160
col ACTIVITY_NAME format a30
col ACTIVITY_STATUS format a20
col error_dtl format a80
select activity_name, activity_status,  error_dtl from dwc_intra_etl_activity;

If all models are created successfully, the activity_status is all "COMPLETED-SUCCESS". If the activity_status is "COMPLETED-ERROR" for a certain step, check the ERROR_DTL column, and fix the problem accordingly.

Some common error messages from ERROR_DTL and ACTIVITY_NAME are listed below.

ORA-20991: Message not available ... [Language=ZHS]CURRENT_MONTH_KEY

This error may happen when there is not enough data in the DWR_BSNS_MO table. For example, if the calendar data is populated with 2004~2009 data, the mining model refresh for Year 2010 may result in this error.

To fix this error, execute the Oracle Communications Data Model calendar utility script again to populate the calendar with sufficient data. For example:

Execute Calendar_Population.run('2005-01-01',10);

See:

Oracle Communications Data Model Reference for information on the calendar population utility script.

Message not available ... [Language=ZHS]

'ZHS' is a code for a language. The language name it relates to can appear as different name depending on the database environment. This error happens when ocdm_sys.DWC_MESSAGE.LANGUAGE does not contain messages for the current language.

Check the values in the DWC_MESSAGE table and, if required, update to the language code specified by the Oracle session variable USERENV('lang').

ORA-40113: insufficient number of distinct target values, for "create_churn_svm_model"

This error happens when the target column for the training model contains only one value or no value when it is expecting more than one value.

For example, for the churn svm model, the target column is:

ocdm_mining.DMV_CUST_CHRN_SRC_PRD.chrn_ind

To troubleshoot this error:

1. Execute a SQL query to check if there are enough values in this column.
Using the churn svm model as an example, issue the following statement.

select chrn_ind, count(*) from DMV_CUST_CHRN_SRC_PRD  group by chrn_ind;

The following is a result of the query.

C   COUNT(*)
- ----------
1       1228
0             4911

2. Check the following tables to ensure customer churn indicators are set properly:

- ocdm_sys.dwr_cust.chrn_dt should contain the value for the churned customers.

- ocdm_sys.dwd_acct_sttstc should contain the correct value for each customer in the most recent six months.

3. Execute the following statement to refresh the mining source materialized views in the ocdm_mining schema:

exec pkg_ocdm_mining.refresh_mining_source;

ORA-40112:insufficient number of valid data rows, for "create_ltv_glmr_model"

For this model, target column is OCDM_MINING.DMV_CUST_LTV_PRDCT_SRC.TOT_PYMT_RVN.

To troubleshoot this error:

1. Execute the following SQL statement:

select count(TOT_PYMT_RVN) from DMV_CUST_LTV_PRDCT_SRC;

2. Check to see that the value returned by this query is greater than 0 (zero) and similar to number of customers. If the number is 0 or too small, check the Oracle Communications Data Model intra-ETL execution status as described in "Monitoring PARALLEL DML Executions".

ORG-11130:no data found in the collection, for "create_sentiment_svm_model"

This error occurs when there is not enough data in the source table for Text sentiment model training: ocdm_mining.dm_cust_cmmnt.

To ensure that some text is loaded for customer sentiment analysis:

1. Issue the following SQL statement.

Select OVRAL_RSLT_CD, count(CUST_COMMENT) from DWB_EVT_PRTY_INTRACN
group by OVRAL_RSLT_CD;

2. Check the number of text comments from the customer interaction table DWB_EVT_PRTY_INTRACN.

3. If there is not enough data in the customer interaction table, check the ETL logic from the source system to the Oracle Communications Data Model.

6 Report and Query Customization

This chapter provides information about creating reports, queries, and dashboards against the data in a Oracle Communications Data Model warehouse. It contains the following topics:

- Reporting Approaches in Oracle Communications Data Model

- Customizing Oracle Communications Data Model Sample Reports

- Writing Your Own Queries and Reports

- Optimizing Star Queries

- Troubleshooting Oracle Communications Data Model Report Performance

- Examples: As Is and As Was Reporting

- Tutorial: Creating a New Oracle Communications Data Model Dashboard

- Tutorial: Creating a New Oracle Communications Data Model Report

Reporting Approaches in Oracle Communications Data Model

There are two main approaches to creating reports from data in an Oracle Communications Data Model warehouse:

- Relational Reporting
With relational reporting, you create reports against the analytical layer entities using the fact entities as the center of the star with the reference entities (that is, DWR_ and DWL_ tables) acting as the dimensions of the star. Typically the fact entities include the derived and aggregate entities (that is, DWD_ and DWA_ tables). However in some cases, you may need to use the base entities (that is, DWB_ tables) along with the reference tables to generate more detailed reports.

The reference tables (that is, DWR_ tables) typically represent dimensions which contain a business hierarchy and are present in the form of snowflake entities containing a table for each level of the hierarchy. This allows us to attach the appropriate set of reference entities for the multiple subject area and fact entities composed of differing granularity.

For example, you can use the set of tables comprising DWR_DAY as well as DWR_BSNS_MO, DWR_BSNS_QTR, DWR_BSNS_YR tables to query against a DAY level CDR Wireless entity such as DWD_VOI_CALL_DAY. On the other hand, you need to use the higher level snowflakes at Month level and above such as DWR_BSNS_MO, DWR_BSNS_QTR, DWR_BSNS_YR in order to query against the MONTH level CDR Wireless entity such as DWA_VOI_CALL_MO.

The lookup tables (that is tables, with the DWL_ prefix) represent the simpler dimensions comprising single level containing a flat list of values. Typically, most reporting tools add a superficial top level to the dimension. These could be individual tables starting with DWL_ or views (also named DWL_) on DWL_CODE_MASTER that break out different code types into separate dimensions.

- OLAP Reporting
With OLAP reporting, you access Oracle OLAP cubes using SQL against the dimension and cube (fact) views. Cubes and dimensions are represented using a star schema design. Dimension views form a constellation around the cube (or fact) view. The dimension and cube views are relational views with names ending with _VIEW. Typically, the dimension view used in the reports is named dimension_hierarchy_VIEW and the cube view is named cube_VIEW.

Unlike the corresponding relational dimension entities stored in DWR_ tables, the OLAP dimension views contains information relating to the whole dimension including all the levels of the hierarchy logically partitioned on the basis of a level column (identified as level_name). On a similar note, the cube views also contain the facts pertaining to the cross-combination of the levels of individual dimensions which are part of the cube definition. Also the join from the cube view and the dimension views are based on the dimension keys along with required dimension level filters.

Although the OLAP views are also modeled as a star schema, there are certain unique features to the OLAP reporting methodology which requires special modeling techniques in Oracle Business Intelligence Suite Enterprise Edition.

The rest of this chapter explains how to create Oracle Communications Data Model reports. For examples of Oracle Communications Data Model reports, see:

- "Examples: As Is and As Was Reporting"

- "Tutorial: Creating a New Oracle Communications Data Model Dashboard"

- "Tutorial: Creating a New Oracle Communications Data Model Report"

- The sample reports provided with Oracle Communications Data Model that are documented in Oracle Communications Data Model Reference.

- The Oracle By Example tutorial, entitled "Using Oracle OLAP 11g With Oracle BI Enterprise Edition". To access the tutorial, open the Oracle Learning Library in your browser by following the instructions in "Oracle Technology Network"; and, then, search for the tutorials by name.

Customizing Oracle Communications Data Model Sample Reports

Sample reports and dashboards are delivered with Oracle Communications Data Model. These sample reports illustrate the analytic capabilities provided with Oracle Communications Data Model -- including the OLAP and data mining capabilities.

See:

Oracle Communications Data Model Installation Guide for more information on installing the sample reports and deploying the Oracle Communications Data Model RPD and webcat on the Business Intelligence Suite Enterprise Edition instance.

The sample reports were developed using Oracle Business Intelligence Suite Enterprise Edition which is a comprehensive suite of enterprise business intelligence products that delivers a full range of analysis and reporting capabilities. Thus, the reports also illustrate the ease with which you can use Oracle Business Intelligence Suite Enterprise Edition Answers and Dashboard presentation tools to create useful reports.

You can use Oracle Business Intelligence Suite Enterprise Edition Answers and Dashboard presentation tools to customize the predefined sample dashboard reports:

- Oracle BI Answers. Provides end user ad hoc capabilities in a pure Web architecture. Users interact with a logical view of the information -- completely hidden from data structure complexity while simultaneously preventing runaway queries. Users can easily create charts, pivot tables, reports, and visually appealing dashboards.

- Oracle BI Interactive Dashboards. Provide any knowledge worker with intuitive, interactive access to information. The end user can be working with live reports, prompts, charts, tables, pivot tables, graphics, and tickers. The user has full capability for drilling, navigating, modifying, and interacting with these results.

See:

Oracle Communications Data Model Reference for detailed information on the sample reports.

Note:

The reports and dashboards that are used in examples and delivered with Oracle Communications Data Model are provided only for demonstration purposes. They are not supported by Oracle.

Writing Your Own Queries and Reports

The ocdm_sys schema defines the relational tables and views in Oracle Communications Data Model. You can use any SQL reporting tool to query and report on these tables and views.

Oracle Communications Data Model also supports On Line Analytic Processing (OLAP) reporting using OLAP cubes defined in the ocdm_sys schema. You can query and write reports on OLAP cubes by using SQL tools to query the views that are defined for the cubes or by using OLAP tools to directly query the OLAP components.

See also:

"Reporting Approaches in Oracle Communications Data Model", "Oracle OLAP Cube Views", and the discussion on querying dimensional objects in Oracle OLAP User's Guide.

Example 6-1 Creating a Relational Query

For example, assume that you want to know the total call minutes for the top ten customers in the San Francisco area for March 2009. To answer this question, you might have to query the tables described in the following table.

Entity Name

Table Name

Description

WIRELESS CALL EVENT

DWB_WRLS_CALL_EVT

Occurrences of the wireless call.

CUSTOMER

DWR_CUST

Individual customers

ADDRESS LOCATION

DWR_ADDR_LOC

All addresses. The table has levels as country, state, city, address, and so on.

GEOGRAPHY CITY

DWR_GEO_CITY

The CITY level of the GEOGRAPHY hierarchy.

To make the query, you execute the following SQL statement.

SELECT cust_key, tot_call_min FROM
(select         round(sum(call.call_drtn)/60,2) tot_call_min , call.cust_key
from    DWB_WRLS_CALL_EVT call,
DWR_CUST     cust,
DWR_ADDR_LOC addr,
DWR_GEO_CITY city
Where to_date(to_char(call.evt_begin_dt,'MON-YY'),'MON-YY') like        to_date('MAR-07','MON-YY')
and cust.cust_key = call.cust_key
and cust.addr_loc_key = addr.addr_loc_key
and addr.geo_city_key = city.geo_city_key
and initcap(city.geo_city_name)='San Francisco'
group by call.cust_key
order by 1 desc) WHERE ROWNUM < 10;

The result of this query is shown below.

CUST_KEY TOT_CALL_MIN
---------- ------------
3390        101.6
4304       100.25
4269        97.37
4152        93.02
4230        92.97
4157        92.95
3345        91.62
4115        48.43
4111        44.48

Optimizing Star Queries

A typical query in the access layer is a join between the fact table and some number of dimension tables and is often referred to as a star query. In a star query each dimension table is joined to the fact table using a primary key to foreign key join. Normally the dimension tables do not join to each other.

Typically, in this kind of query all of the WHERE clause predicates are on the dimension tables and the fact table. Optimizing this type of query is very straight forward.

In order to optimize, you simply:

- Create a bitmap index on each of the foreign key columns in the fact table or tables

- Set the initialization parameter STAR_TRANSFORMATION_ENABLED to TRUE.

This enables the optimizer feature for star queries which is off by default for backward compatibility.

If your environment meets these two criteria, your star queries should use a powerful optimization technique that rewrites or transforms your SQL called star transformation. Star transformation executes the query in two phases:

1. Retrieves the necessary rows from the fact table (row set).

2. Joins this row set to the dimension tables.

The rows from the fact table are retrieved by using bitmap joins between the bitmap indexes on all of the foreign key columns. The end user never needs to know any of the details of STAR_TRANSFORMATION, as the optimizer automatically chooses STAR_TRANSFORMATION when it is appropriate.

Example 6-2, "Star Transformation" gives the step by step process to use STAR_TRANSFORMATION to optimize a star query.

Example 6-2 Star Transformation

A business question that could be asked against the star schema in Figure 4-1, "Star Schema Diagram" would be "What was the total number of umbrellas sold in Boston during the month of May 2008?"

1. The original query.

select SUM(quantity_sold) total_umbrellas_sold_in_Boston
From Sales s, Customers c, Products p, Times t
Where s.cust_id=cust_id
And s.prod_id = p.prod_id
And s.time_id=t.time_id
And c.cust_city='BOSTON'
And p.product='UMBRELLA'
And t.month='MAY'
And t.year=2008;

As you can see all of the where clause predicates are on the dimension tables and the fact table (Sales) is joined to each of the dimensions using their foreign key, primary key relationship.

2. Take the following actions:

1. Create a bitmap index on each of the foreign key columns in the fact table or tables

2. Set the initialization parameter STAR_TRANSFORMATION_ENABLED to TRUE.

3. The rewritten query. Oracle rewrites and transfers the query to retrieve only the necessary rows from the fact table using bitmap indexes on the foreign key columns

select SUM(quantity_sold
From Sales
Where cust_id IN
(select c.cust_id From Customers c Where c.cust_city='BOSTON')
And s.prod_id IN
(select p.prod_id From Products p Where  p.product='UMBRELLA')
And s.time_id IN
(select t.time_id From Times(Where t.month='MAY' And tyear=2008);

By rewriting the query in this fashion you can now leverage the strengths of bitmap indexes. Bitmap indexes provide set based processing within the database, allowing you to use various fact methods for set operations such as AND, OR, MINUS, and COUNT. So, you use the bitmap index on time_id to identify the set of rows in the fact table corresponding to sales in May 2008. In the bitmap the set of rows are actually represented as a string of 1's and 0's. A similar bitmap is retrieved for the fact table rows corresponding to the sale of umbrellas and another is accessed for sales made in Boston. At this point there are three bitmaps, each representing a set of rows in the fact table that satisfy an individual dimension constraint. The three bitmaps are then combined using a bitmap AND operation and this newly created final bitmap is used to extract the rows from the fact table needed to evaluate the query.

4. Using the rewritten query, Oracle joins the rows from fact tables to the dimension tables.
The join back to the dimension tables is normally done using a hash join, but the Oracle Optimizer selects the most efficient join method depending on the size of the dimension tables.

The following figure shows the typical execution plan for a star query when STAR_TRANSFORMATION has kicked in. The execution plan may not look exactly how you imagined it. You may have noticed that you can see that there is no join back to the customer table after the rows have been successfully retrieved from the Sales table. If you look closely at the select list, you can see that there is not anything actually selected from the Customers table so the optimizer knows not to bother joining back to that dimension table. You may also notice that for some queries even if STAR_TRANSFORMATION does kick in it may not use all of the bitmap indexes on the fact table. The optimizer decides how many of the bitmap indexes are required to retrieve the necessary rows from the fact table. If an additional bitmap index would not improve the selectivity the optimizer does not use it. The only time you see the dimension table that corresponds to the excluded bitmap in the execution plan is during the second phase or the join back phase.

Troubleshooting Oracle Communications Data Model Report Performance

Take the following actions to identify problems generating a report created using Oracle Business Intelligence Suite Enterprise Edition:

1. In the (Online) Oracle BI Administrator Tool, select Manage, then Security, then Users, and then ocdm.
Ensure that the value for Logging level is 7.

2. Open the Oracle Communications Data Model Repository, select Manage, and then Cache.

3. In the right-hand pane of the Cache Manager window, select all of the records, then right-click and select Purge.

4. Run the report or query that you want to track using the SQL log.

5. Open the query log file (NQQuery.log) under OracleBI\server\Log.
The last query SQL is the log of the report you have just run. If an error was returned in your last accessed report, there is an error at the end of this log.

For example:

- Error: Query Status: Query Failed: [nQSError: 15018] Incorrectly defined logical table source (for fact table Customer Mining) does not contain mapping for [Customer.Cell Phone Number, Customer.Customer Segment Name, Customer.Party Name].
Meaning: This error occurs when there is a problem in the Business layer in your Oracle Business Intelligence Suite Enterprise Edition repository. Check the mappings.

Action: Check the mapping for Customer.Cell Phone Number, Customer.Customer Segment Name, and Customer.Party Name.

- Error: Query Status: Query Failed: [encloser: 17001] Oracle Error code: 942, message: ORA-00942: table or view does not exist.
Meaning: This error occurs when the physical layer in your Oracle Business Intelligence Suite Enterprise Edition repository has the table which actually does not exist in database.

Action: To find out which table has problem: Copy the SQL query to database environment and execute the query. The table which does not exist is marked out by the database client.

- Error: Query Status: Query Failed: [nQSError: 17001] Oracle Error code: 12545, message: ORA-12545: connect failed because target host or object does not exist.
Meaning: This error occurs when the Database is not connected.

Action: Check connecting information in physical layer and ODBC connection to ensure that the repository is connecting to the correct database.

Examples: As Is and As Was Reporting

An As Is query has the following characteristics:

- The resulting report shows the data as it happened.

- The snowflake dimension tables are also joined using the surrogate key columns (that is the primary key and foreign key columns).

- The fact table is joined with the dimension tables (at leaf level) using the surrogate key column.

- Slowly-changing data in the dimensions are joined with their corresponding fact records and are presented individually.

- It is possible to add up the components if the different versions share similar characteristics.

An As Was query (also known as point-in-time analysis) has the following characteristics:

- The resulting report shows the data that would result from freezing the dimensions and dimension hierarchy at a specific point in time.

- Each snowflake table is initially filtered by applying a point-in-time date filter which selects the records or versions which are valid as of the analysis date. This structure is called the point-in-time version of the snowflake.

- The filtered snowflake is joined with an unfiltered version of itself by using the natural key. All of the snowflake attributes are taken from the point-in-time v3rsion alias. The resulting structure is called the composite snowflake.

- A composite dimension is formed by joining the individual snowflakes on the surrogate key.

- The fact table is joined with the composite dimension table at the leaf level using the surrogate key column.

- The point-in-time version is super-imposed on all other possible SCD versions of the same business entity -- both backward as well as forward in time. Joining in this fashion gives the impression that the dimension is composed of only the specific point-in-time records.

- All of the fact components for various versions add up correctly due to the super-imposition of point-in-time attributes within the dimensions.

Based on the Data used for the examples, the following examples illustrate the characteristics of As Is and As Was queries:

- Example 6-3, "As Is Query for Tax Collection Split by Marital Status"

- Example 6-4, "As Was Queries for Tax Collection Split by Marital Status"

- Example 6-5, "As Is Query for Tax Collection Data Split by County"

- Example 6-6, "As Was Queries for Tax Collection Data Split by County"

Data used for the examples

Assume that your data warehouse has a Customer table, a County, and a TaxPaid fact table. As of January 1, 2011, these tables include the values shown below.

Customer Table

Cust Id

Cust Cd

Cust Nm

Gender

M Status

County Id

County Cd

Country Nm

...

Eff Frm

Eff To

101

JoD

John Doe

Male

Single

5001

SV

Sunnyvale

...

1-Jan-11

31-Dec-99

102

JaD

Jane Doe

Female

Single

5001

SV

Sunnyvale

...

1-Jan-11

31-Dec-99

103

JiD

Jim Doe

Male

Married

5002

CU

Cupertino

...

1-Jan-11

31-Dec-99

County Table

County Id

County CD

County Nm

Population

...

Eff Frm

Eff To

5001

SV

Sunnyvale

Very High

...

1-Jan-11

31-Dec-99

5002

CU

Cupertino

High

...

1-Jan-11

31-Dec-99

TaxPaid Table

Cust Id

Day

Tax Type

Tax

101

1-Jan-11

Professional Tax

100

102

1-Jan-11

Professional Tax

100

103

1-Jan-11

Professional Tax

100

Assume that the following events occurred in January 2011:

- On January 20, 2011, Jane Doe marries.

- On Jan 29, 2011, John Doe moves from Sunnyvale to Cupertino.

Consequently, as shown below, on February 1, 2011, the Customer and TaxPaid tables have new data while the values in the County table stay the same.

Customer table

Cust Id

Cust Cd

Cust Nm

Gender

M Status

County Id

County Cd

Country Nm

...

Eff Frm

Eff To

101

JoD

John Doe

Male

Single

5001

SV

Sunnyvale

...

1-Jan-11

29-Jan-11

102

JaD

Jane Doe

Female

Single

5001

SV

Sunnyvale

...

1-Jan-11

20-Jan-11

103

JiD

Jim Doe

Male

Married

5002

CU

Cupertino

...

1-Jan-11

31-Dec-99

104

JaD

Jane Doe

Female

Married

5001

SV

Sunnyvale

...

21-Jan-11

31-Dec-99

105

JoD

John Doe

Male

Single

5002

CD

Cupertino

...

30-Jan-11

31-Dec-99

County table

County Id

County CD

County Nm

Population

...

Eff Frm

Eff To

5001

SV

Sunnyvale

Very High

...

1-Jan-11

31-Dec-99

5002

CU

Cupertino

High

...

1-Jan-11

31-Dec-99

TaxPaid Table

Cust Id

Day

Tax Type

Tax

101

1-Jan-11

Professional Tax

100

102

1-Jan-11

Professional Tax

100

103

1-Jan-11

Professional Tax

100

105

1-Feb-11

Professional Tax

100

104

1-Feb-11

Professional Tax

100

103

1-Feb-11

Professional Tax

100

Example 6-3 As Is Query for Tax Collection Split by Marital Status

Assuming the Data used for the examples, to show the tax collection data split by martial status, the following SQL statement that joins the TaxPaid fact table and the Customer dimension table on the cust_id surrogate key and the Customer and County snowflakes on the cnty_id surrogate key.

SELECT cust.cust_nm, cust.m_status, SUM(fct.tx)
FROM taxpaid fct, customer cust, county cnty
WHERE fct.cust_id = cust.cust_id
AND cust.cnty_id = cnt.cnt_id
GROUP BY cust.cust_nm, cust.m_status
ORDER BY 1,2,3;

The results of this query are shown below. Note that there are two rows for Jane Doe; one row for a marital status of Married and another for a marital status of Single.

Cust Nm

M Status

Tax

Jane Doe

Married

100

Jane Doe

Single

100

Jim Doe

Married

200

John Doe

Single

200

Example 6-4 As Was Queries for Tax Collection Split by Marital Status

Assuming the Data used for the examples, issue the following SQL statement to show the tax collection data split by marital status using an analysis date of January 15, 2011.

select
cust.cust_nm, cust.m_status, sum(fct.tax)
from
TaxPaid fct,
(
select
cust_act.cust_id, cust_pit.cust_cd, cust_pit.cust_nm,
cust_pit.m_status, cust_pit.gender,
cust_pit.cnty_id, cust_pit.cnty_cd, cust_pit.cnty_nm
from Customer cust_act
inner join (
select
cust_id, cust_cd, cust_nm,
m_status, gender,
cnty_id, cnty_cd, cnty_nm
from Customer cust_all
where to_date('15-JAN-2011', 'DD-MON-YYYY') between eff_from and eff_to
) cust_pit
on (cust_act.cust_cd = cust_pit.cust_cd)
) cust,
(
select
cnty_act.cnty_id, cnty_pit.cnty_cd, cnty_pit.cnty_nm
from County cnty_act
inner join (
select
cnty_id, cnty_cd, cnty_nm
from County cnty_all
where to_date('15-JAN-2011', 'DD-MON-YYYY') between eff_from and eff_to
) cnty_pit
on (cnty_act.cnty_cd = cnty_pit.cnty_cd)
) cnty
where fct.cust_id = cust.cust_id
and cust.cnty_id = cnty.cnty_id
GROUP BY cust.cust_nm, cust.m_status
order by 1,2,3;

The results of this query are shown below. Since Jane Doe was single on January 15, 2011 (the analysis date), all tax for Jane Doe is accounted under her Single status.

Cust Nm

M Status

Tax

Jane Doe

Single

200

Jim Doe

Married

200

John Doe

Single

200

Assume instead that you issued the exact same query except that for the to_date phrase you specify 09-FEB-2011 rather than 15-JAN-2011. Since Jane Doe was single on February 9, 2011, then, as shown below all tax for Jane Doe would be accounted under her Married status.

Cust Nm

M Status

Tax

Jane Doe

Married

200

Jim Doe

Married

200

John Doe

Single

200

Example 6-5 As Is Query for Tax Collection Data Split by County

Assuming the Data used for the examples, issue the following SQL statement to show the tax collection data split by county.

SELECT cust.cust_nm, cnty.cnty_nm, SUM(fct.tax)
FROM TaxPaid fct, customer cust, county cnty
WHERE fct.cust_id = cust.cust_id
AND cust.cnty_id = cnty.cnty_ID
GROUP BY cut.cust_nm, cnty.cnty_nm
ORDER BY 1,2,3;

The results of this query are shown below. Note that since John Doe lived in two different counties, there are two rows of data for John Doe.

Cust Nm

County Nm

Tax

Jane Doe

Sunnyvale

200

Jim Doe

Cupertino

200

John Doe

Cupertino

100

John Doe

Sunnyvale

100

Example 6-6 As Was Queries for Tax Collection Data Split by County

Assuming the Data used for the examples, issue the following SQL statement to show the tax collection data split by county using an analysis date of January 15, 2011.

select
cust.cust_nm, cnty.cnty_nm, sum(fct.tax)
from
TaxPaid fct,
(
select
cust_act.cust_id, cust_pit.cust_cd, cust_pit.cust_nm,
cust_pit.m_status, cust_pit.gender,
cust_pit.cnty_id, cust_pit.cnty_cd, cust_pit.cnty_nm
from Customer cust_act
inner join (
select
cust_id, cust_cd, cust_nm,
m_status, gender,
cnty_id, cnty_cd, cnty_nm
from Customer cust_all
where to_date('15-JAN-2011', 'DD-MON-YYYY') between eff_from and eff_to
) cust_pit
on (cust_act.cust_cd = cust_pit.cust_cd
) cust,
(
select
cnty_act.cnty_id, cnty_pit.cnty_cd, cnty_pit.cnty_nm
from County cnty_act
inner join (
select
cnty_id, cnty_cd, cnty_nm
from County cnty_all
where to_date('15-JAN-2011', 'DD-MON-YYYY') between eff_from and eff_to
) cnty_pit
on (cnty_act.cnty_cd = cnty_pit.cnty_cd)
) cnty
where fct.cust_id = cust.cust_id
and cust.cnty_id = cnty.cnty_id
GROUP BY cust.cust_nm, cnty.cnty_nm
order by 1,2,3;

The results of this query are shown below. Note that since John Does was in Sunnyvale as of the analysis date of January 15, 2011, all tax for John Doe is accounted for under the Sunnyvale county.

Cust Nm

County Nm

Tax

Jane Doe

Sunnyvale

200

Jim Doe

Cupertino

200

John Doe

Sunnyvale

200

Assume instead that you issued the exact same query except that for the to_date phrase you specify 09-FEB-2011 rather than 15-JAN-2011. Since John Doe lived in Cupertino on February 9, 2011, then, as shown below all tax for John Doe would be accounted under Cupertino.

Cust Nm

County Nm

Tax

Jane Doe

Sunnyvale

200

Jim Doe

Cupertino

200

John Doe

Cupertino

200

Tutorial: Creating a New Oracle Communications Data Model Dashboard

This tutorial explains how to create a new dashboard based on the Oracle Communications Data Model webcat included with the sample Oracle Business Intelligence Suite Enterprise Edition reports delivered with Oracle Communications Data Model.

See:

Oracle Communications Data Model Installation Guide for more information on installing the sample reports and deploying the Oracle Communications Data Model RPD and webcat on the Business Intelligence Suite Enterprise Edition instance.

In this example assume that you want to create a dashboard named "Dropped call and Failed Call", and put both "Dropped Call Rate Report" and "Failed Call Rate Report" into this new dashboard.

To create a dashboard, take the following steps:

1. In the browser, open the login page at http://servername:9704/analytics where servername is the server on which the webcat is installed.

2. Login with username of ocdm, and provide the password.
Then, click newDashboard to create an Oracle Business Intelligence Suite Enterprise Edition dashboard.

3. Input name and description, save it to the Network Health folder. Click OK.

4. In the Catalog view, expand the Network Health Analysis folder. You can see the Failed Call Rate Report and Dropped Call Rate Report.

5. Drag the Failed Call Rate Report and Dropped Call Rate Report from the Catalog view into the right panel.

6. You can change the layout of this section to organize the two reports by horizontal or vertical.

Note that the page name is still "Page1" so you must change it.

7. To change the page name:

1. Select the Dashboard.

2. In Dashboard Properties window, click Change Name.

3. Change the name to "Dropped Call Rate and Failed Call Rate", then click OK.

8. Click Save on the top of the dashboard. Now you have a new dashboard.

Oracle by Example:

For more information on creating dashboards see the "Creating Analyses and Dashboards 11g" OBE tutorial.
To access the tutorial, open the Oracle Learning Library in your browser by following the instructions in "Oracle Technology Network"; and, then, search for the tutorial by name.

Tutorial: Creating a New Oracle Communications Data Model Report

This tutorial explains how to create a new report based on the Oracle Communications Data Model webcat included with the sample Oracle Business Intelligence Suite Enterprise Edition reports delivered with Oracle Communications Data Model.

See:

Oracle Communications Data Model Installation Guide for more information on installing the sample reports and deploying the Oracle Communications Data Model RPD and webcat on the Business Intelligence Suite Enterprise Edition instance.

In this example, assume that you want to create a report named "Dropped call vs. Failed Call" to put both dropped call rate and failed call rate in one report.

To create a this new report, take the following steps:

1. In the browser, open the login page at http://servername:9704/analytics where servername is the server on which the webcat is installed.

2. Login with username of ocdm, and provide the password.
Then, click newAnalysis to create an Oracle Business Intelligence Suite Enterprise Edition report.

3. Select Subject Area, then select ODWT to create a relational report.

4. Drag and put the dimension and fact columns into the Select Columns panel.

5. Select the Results tab to view the report

6. Click New View to add a chart into report.

7. Click Save to save this report to the Network Health Analysis folder

Oracle by Example:

For more information on creating a report, see the "Creating Analyses and Dashboards 11g" OBE tutorial.
To access the tutorial, open the Oracle Learning Library in your browser by following the instructions in "Oracle Technology Network"; and, then, search for the tutorial by name.

A Exadata and the Oracle Communications Data Model

This appendix provides information you can use to bring the benefits of Exadata to your Oracle Communications Data Model warehouse. It contains the following topics:

- Benefits of Running Oracle Communications Data Model on an Exadata Storage Server

- Suggested Practices for Oracle Communications Data Model Warehouse in Exadata

Benefits of Running Oracle Communications Data Model on an Exadata Storage Server

An Exadata Storage Server offers the following capabilities that provide major benefits when you run Oracle Communications Data Model:

- Exadata: A Preconfigured Balanced System

- Exadata Smart Flash Cache

- Exadata Storage Capacity, Performance, Bandwidth and IOPS

- Exadata Smart Scan Processing and Storage Index

- Other Exadata Optimization Techniques for Data Warehousing

Exadata: A Preconfigured Balanced System

The Database Machine is a pre-configured system ready to be turned on on day one, taking significant integration work, cost and time out of the database deployment process. The Exadata Storage Server (Exadata storage or Exadata cells) is used as the storage for the Oracle Database in the Database Machine and is used to grow existing Database Machine deployments. The benefit of a common infrastructure to deploy a database for any application, whether OLTP, data warehouse, a mix of the two, or as a platform for consolidation of several databases, creates tremendous opportunities for efficiencies in the datacenter. The Database Machine is truly a "cloud in box".

The new top-of-the-line Exadata Database Machine X2-8 combines the best of scale-up and scale-out architectures by delivering a grid architecture containing large SMP database servers. Historically, a 64-core SMP required a full rack of equipment by itself, and was difficult to scale out further.The hardware components of the Exadata Storage Server (also referred to as an Exadata cell) were carefully chosen to match the needs of high performance database processing. The Exadata software is optimized to take the best possible advantage of the hardware components and Oracle Database. Each Exadata cell delivers outstanding I/O performance and bandwidth to the database.

Demos:

For more information on Exadata, see the following demos.

- Exadata and Database Machine Version 2 Series - 4 of 25: Exadata Process Introduction

- Exadata and Database Machine Version 2 Series - 8 of 25: Exadata Cell Configuration

- Exadata and Database Machine Version 2 Series - 22 of 25: Bulk Data Loading with Database Machine

To access the demos, sign into OTN and open the Oracle Learning Library in your browser by following the instructions in "Oracle Technology Network"; and, then, search for the demos by name.

Exadata Smart Flash Cache

Each Exadata cell comes with 384 GB of Exadata Smart Flash Cache which means in the Database Machine X2-8 and Full Rack X2-2 there is 5.3 TB of Flash - larger than most databases. This solid state storage delivers dramatic performance advantages with Exadata storage. Exadata Smart Flash Cache provides a ten-fold improvement in response time for reads over regular disk, a hundred-fold improvement in IOPS for reads over regular disk, and is a less expensive higher capacity alternative to memory. Overall it delivers a ten-fold increase performing a blended average of read and write operations.

The Exadata Smart Flash Cache manages active data from regular disks in the Exadata cell - but the cache is not managed in a simple Least Recently Used (LRU) fashion. The Exadata Storage Server Software in cooperation with the Oracle Database keeps track of data access patterns and knows what and how to cache data and avoid polluting the cache. This functionality is all managed automatically and does not require manual tuning. If there are specific tables or indexes that are known to be key to the performance of a database application they can optionally be identified and pinned in cache.

Exadata Storage Capacity, Performance, Bandwidth and IOPS

The Oracle Exadata Storage Servers comes with either twelve 600 GB 15,000 RPM High Performance SAS disks or twelve 2 TB 7,200 RPM High Capacity SAS disks.

The storage capacity of each model of the Database Machine is shown in the following table.

Table A-1 Database Machine Storage Capacity

Database Machine X2-8 and X2-2 Full Rack

Database Machine X2-2 Half Rack

Database Machine X2-2 Quarter Rack

Exadata Smart Flash Cache

5.3 TB

2.6 TB

1.1 TB

Raw Disk Capacity: High Performance SAS

100 TB

50 TB

21 TB

Raw Disk Capacity: High Capacity SAS

336 TB

168 TB

72 TB

Useable Capacity: High Performance SAS

Up to 45 TB

Up to 22.5 TB

Up to 9.25 TB

Useable Capacity: High Capacity SAS

Up to 150 TB

Up to 75. TB

Up to 31.5 TB

Note:

When calculating raw disk capacity, 1 TB = 1 trillion bytes. Actual formatted capacity is less. Useable capacity available for databases is computed after mirroring (Oracle ASM normal redundancy) and leaving one empty disk to automatically handle disk failures.

The performance that each cell delivers is extremely high due to the Exadata Smart Flash Cache.

The Exadata software can simultaneously scan from Flash and disk to maximize bandwidth. The automated caching within Flash enables each Exadata cell to deliver up to 5.4 GB per second bandwidth and 125,000 IOPS when accessing uncompressed data. When data is stored in compressed format, the amount of user data capacity, the amount of data bandwidth and IOPS achievable, often increases up to ten times, or more which represents a significant improvement over traditional storage devices used with the Oracle Database.

The performance characteristics of each model of the Database Machine are depicted in the following table.

Table A-2 Database Machine I/O Performance

Database Machine X2-8 and X2-2 Full Rack

Database Machine X2-2 Half Rack

Database Machine X2-2 Quarter Rack

Raw Disk Data Bandwidth: High Performance SAS (without data compression)

Up to 25 GB per second

Up to 12.5 GB per second

Up to 5.4 GB per second

Raw Disk Data Bandwidth: High Capacity SAS (without data compression)

Up to 14 GB per second

Up to 7.0 GB per second

Up to 3.0 GB per second

Raw Flash Data Bandwidth: High Performance SAS (without data compression)

Up to 75 GB per second

Up to 37.5 GB per second

Up to 16 GB per second

Raw Flash Data Bandwidth: High Capacity SAS (without data compression)

Up to 64 GB per second

Up to 32 GB per second

Up to 13.5 GB per second

Flash Cache IOPS

Up to 1,500,000

Up to 750,000

Up to 375,000

Disk IOPS: High Performance SAS

Up to 50,000

Up to 25,000

Up to 10,800

Disk IOPS: High Capacity SAS

Up to 25,000

Up to 12,500

Up to 5,400

Exadata Smart Scan Processing and Storage Index

As is often the case with the large queries, the predicate filters out most of the rows read. Yet all the blocks from the table must be read, transferred across the storage network and copied into memory. Many more rows are read into memory than required to complete the requested SQL operation which generates a large number of data transfers which consume bandwidth and impact application throughput and response time.

With Exadata storage, database operations are handled much more efficiently. Queries that perform table scans can be processed within Exadata storage with only the required subset of data returned to the database server. Row filtering, column filtering and some join processing (among other functions) are performed within the Exadata storage cells. When this takes place only the relevant and required data is returned to the database server.

The Oracle Database and Exadata server cooperatively execute various SQL statements. Moving SQL processing off the database server frees server CPU cycles and eliminates a massive amount of bandwidth consumption which is then available to better service other requests. SQL operations run faster, and more of them can run concurrently because of less contention for the I/O bandwidth.

The following topics provide detailed information on the various SQL operations that benefit from the use of Exadata:

- Smart Scan Predicate Filtering

- Smart Scan Column Filtering

- Smart Scan Join Processing

- Smart Scan Processing of Encrypted Tablespaces and Columns

- Storage Indexing

- Offload of Data Mining Model Scoring

- Other Exadata Smart Scan Processing

Smart Scan Predicate Filtering

Exadata enables predicate filtering for table scans. Only the rows requested are returned to the database server rather than all rows in a table. For example, when the following SQL is issued only rows where the employees' hire date is after the specified date are sent from Exadata to the database instance.

SELECT * FROM employee_table WHERE hire_date > '1-Jan-2003'.

This ability to return only relevant rows to the server greatly improves database performance. This performance enhancement also applies as queries become more complicated, so the same benefits also apply to complex queries, including those with subqueries.

Smart Scan Column Filtering

Exadata provides column filtering, also called column projection, for table scans. Only the columns requested are returned to the database server rather than all columns in a table. For example, when the following SQL is issued, only the employee_name and employee_number columns are returned from Exadata to the database kernel.

SELECT employee_name, employee_number FROM employee_table.

For tables with many columns, or columns containing LOBs (Large Objects), the I/O bandwidth saved can be very large. Using both predicate and column filtering dramatically improves performance and reduces I/O bandwidth consumption. In addition, column filtering also applies to indexes, allowing for even faster query performance.

Smart Scan Join Processing

Exadata performs joins between large tables and small lookup tables, a very common scenario for data warehouses with star schemas. Joining large tables and small lookup tables is implemented using Bloom Filters, which are a very efficient probabilistic method to determine whether a row is a member of the desired result set.

Smart Scan Processing of Encrypted Tablespaces and Columns

Smart Scan offload processing of Encrypted Tablespaces (TSE) and Encrypted Columns (TDE) is supported in Exadata storage which increases performance when accessing the most confidential data in the enterprise.

Storage Indexing

Storage Indexes are a very powerful capability provided in Exadata storage that helps avoid I/O operations. The Exadata Storage Server Software creates and maintains a Storage Index (that is, metadata about the database objects) in the Exadata cell. The Storage Index keeps track of minimum and maximum values of columns for tables stored on that cell. When a query specifies a WHERE clause, but before any I/O is done, the Exadata software examines the Storage Index to determine if rows with the specified column value exist in the cell by comparing the column value to the minimum and maximum values maintained in the Storage Index. If the column value is outside the minimum and maximum range, scan I/O for that query is avoided. Many SQL Operations run dramatically faster because large numbers of I/O operations are automatically replaced by a few lookups. To minimize operational overhead, Storage Indexes are created and maintained transparently and automatically by the Exadata Storage Server Software.

Demos:

For an introduction to Exadata Storage Indexes, see the following demos.

- Storage Index in Exadata

- Exadata and Database Machine Version 2 Series - 5 of 25: Hierarchy of Exadata Storage Objects

To access the demos, sign into OTN and open the Oracle Learning Library in your browser by following the instructions in "Oracle Technology Network"; and, then, search for the demos by name.

Offload of Data Mining Model Scoring

Data Mining model scoring is offloaded to Exadata which makes the deployment of data warehouses on Database Machine an even better and more performant data analysis platform. All data mining scoring functions (for example, prediction_probability) are offloaded to Exadata for processing. Offloading data mining scoring functions not only speeds warehouse analysis, but reduce database server CPU consumption and the I/O load between the database server and Exadata storage.

Other Exadata Smart Scan Processing

Two other database operations that are offloaded to Exadata are incremental database backups and tablespace creation. The speed and efficiency of incremental database backups has been significantly enhanced with Exadata. The granularity of change tracking in the database is much finer when Exadata storage is used. Changes are tracked at the individual Oracle block level with Exadata rather than at the level of a large group of blocks which results in less I/O bandwidth being consumed for backups and faster running backups.

With Exadata the create file operation is also executed much more efficiently. For example, when issuing a CREATE TABLESPACE command, instead of operating synchronously with each block of the new tablespace being formatted in server memory and written to storage, an iDB command is sent to Exadata instructing it to create the tablespace and format the blocks. Host memory usage is reduced and I/O associated with the creation and formatting of the tablespace blocks is offloaded. The I/O bandwidth saved with these operations means more bandwidth is available for other business critical work.

Other Exadata Optimization Techniques for Data Warehousing

Beside the benefits of running an Oracle Communications Data Model warehouse on an Exadata Server discussed earlier in this appendix, Exadata offers the following optimization techniques for any data warehouse -- including an Oracle Communications Data Model warehouse:

- Exadata Hybrid Columnar Compression
As discussed in "Types of Data Compression Available", Exadata provides a very advanced compression capability called Exadata Hybrid Columnar Compression (EHCC). EHCC enables the highest levels of data compression and provides enterprises with tremendous cost-savings and performance improvements due to reduced I/O. Average storage savings can range from 10x to 15x depending on how EHCC is used.

See also:

"Exadata Hybrid Columnar Compression"

- I/O Resource Management With Exadata
In data warehousing, or mixed workload environments, you may want to ensure different users and tasks within a database are allocated the correct relative amount of I/O resources. For example you may want to allocate 70% of I/O resources to interactive users on the system and 30% of I/O resources to batch reporting jobs. This allocation is simple to enforce using the DBRM and I/O resource management capabilities of Exadata storage.

An Exadata administrator can create a resource plan that specifies how I/O requests are prioritized by putting the different types of work into service groupings called Consumer Groups. Consumer groups can be defined by many attributes including the username, client program name, function, or length of time the query has been running. After these consumer groups are defined, the user can set a hierarchy of which consumer group gets precedence in I/O resources and how much of the I/O resource is given to each consumer group. This hierarchy determining I/O resource prioritization can be applied simultaneously to both intra-database operations (that is, operations occurring within a database) and inter-database operations (that is, operations occurring among various databases).

In essence, Exadata I/O Resource Manager has solved one challenge traditional storage technology does not address: creating a shared grid storage environment with the ability to balance and prioritize the work of multiple databases and users sharing the storage subsystem. Exadata I/O resource management ensures user-defined Service Level Agreements (SLA) are met for multiple databases sharing Exadata storage so that each database or user gets the correct share of disk bandwidth to meet business objectives.

- Quality of Service (QoS) Management with Exadata
Oracle Exadata QoS Management is an automated, policy-based product that monitors the workload requests for an entire system. It manages the resources that are shared across applications and adjusts the system configuration to keep the applications running at the performance levels needed by your business. It responds gracefully to changes in system configuration and demand, thus avoiding additional oscillations in the performance levels of your applications.

Suggested Practices for Oracle Communications Data Model Warehouse in Exadata

Exadata's features offer the opportunity to modify or tune the data model to increase performance and maximum usage of Exadata. Although, the implementation of the following practices depends on the specific customer application of the Oracle Communications Data Model, these practices should serve as a guideline for determining applicability of these features:

- Index Rich Versus Index Scarce Design

- Exadata Hybrid Columnar Compression

Index Rich Versus Index Scarce Design

Traditional design dictates a the use of indexes on data warehouse tables to aid in performance. In a traditional environment, typically the processing time within the database for performing an index lookup is significantly lower than a full table scan. Because of the lower processing time for index lookup, traditional design is an index rich design where tables have many indexes which are specifically setup based on known or predicted query patterns. In a traditional environment, the main downsides of index rich designs are: the space required for storing the index, the care and feeding of the indexes by the DBA staff, and the temptation to 'over-index' by adding indexes on nearly every column.

Exadata allows for the possibility of index scarce designs. In an index scarce design, indexes are removed or altered invisible to force full table scans on specific tables. Because of the features of Exadata, specifically smart scan and storage indexing, a table scan may return a result faster than walking an index. In addition to the possibility of faster return times, an index-scarce design provides storage savings if the index is removed.

A common scenario though is to first alter the index invisible, which still keeps the index structure and data intact, but accomplishes the equivalent of removing the indexes for processing time. In this scenario, a customer can understand the impact of index-scarce design without risking the need to rebuild indexes.

Also, if the data warehouse is expected to run on a non-Exadata environment, for example in a disaster recovery environment, removing the indexes has significant degradation in that non-Exadata environment.

Exadata Hybrid Columnar Compression

As discussed in "Types of Data Compression Available", Exadata provides a very advanced compression capability called Exadata Hybrid Columnar Compression (EHCC). EHCC enables the highest levels of data compression and provides enterprises with tremendous cost-savings and performance improvements due to reduced I/O. Average storage savings can range from 10x to 15x depending on how EHCC is used.

With the high compression rates resulting from Hybrid Columnar Compression (HCC), customers can achieve significant storage savings. Hybrid Columnar compression, though, is not applicable to all scenarios.

HCC can be applied at a table or partition level and is best applied at that level of granularity.

A typical scenario is to implement a time based information lifecycle management (ILM) using the various compression options including Advanced Compression, Hybrid Columnar Compression Query and Hybrid Columnar Compression Archive. The Oracle Communications Data Model implementation can consider date partitioning on large fact tables and can apply HCC-Q and HCC-A to data that is no longer being updated or no longer being read.

For example, a customer may have the following requirements for Customer Orders:

- Orders can be updated and changed for the first 90 days.

- After 90 days, orders cannot be changed, but are typically used in many reports.

- After 3 years, the data is rarely, if ever, queried, but is required for retention reasons.

A design which used Advanced Compression for partitions containing data less than 90 days old, HCC-Q for partitions with data 90 days to 3 years, and HCC-A for partitions with data older than 3 years maximizes use of all three compression options, while minimizing the risk of performance degradation.

Demos:

For more information on Exadata Hybrid Columnar Compression, see the following demos.

- Exadata and Database Machine Version 2 Series - 2 of 25: Introduction to Exadata Hybrid Columnar Compression

- Exadata and Database Machine Version 2 Series - 18 of 25: Examining Exadata Hybrid Columnar Compression

- Exadata and Database Machine Version 2 Series - 19 of 25: Index Elimination with Exadata

- Hybrid Columnar Compression

To access the demos, sign into OTN and open the Oracle Learning Library in your browser by following the instructions in "Oracle Technology Network"; and, then, search for the demos by name.

See also:

"Data Compression in the Oracle Communications Data Model" and "Compressing Materialized Views".

B Sizing and Configuring an Oracle Communications Data Model Warehouse

This appendix provides information about sizing and configuring an Oracle Communications Data Model warehouse. It contains the following topics:

- Sizing an Oracle Communications Data Model Warehouse

- Configuring a Balanced System for Oracle Communications Data Model

Sizing an Oracle Communications Data Model Warehouse

Businesses now demand more information sooner and are delivering analytics from their Enterprise Data Warehouse (EDW) to an ever-widening set of users and applications. In order to keep up with this increase in demand the EDW must now be near real-time and be highly available. Regardless of the design or implementation of a data warehouse the initial key to good performance lies in the hardware configuration used. This has never been more evident than with the recent increase in the number of data warehouse appliances in the market.

But how do you go about sizing such a system? You must first understand how much throughput capacity is required for your system and how much throughput each individual CPU or core in your configuration can drive, thus the number one task is to calculate the database space requirement in your data warehouse.

There are two data volume estimate resources in a data warehouse environment:

- The estimated raw data extract from source systems. This estimate affects the ETL system configuration and the stage layer database space in data warehouse system. Because this value is determined by your unique OLTP system, you must calculate this information yourself.

- The space needed for data stored to support the objects defined in the default Oracle Communications Data Model schema. This appendix provides information you can use to make this calculation.

Calculation Factors When Making a Data Volume Calculation for an Oracle Communications Data Model Warehouse

Consider the following calculation factors when making a data volume calculation:

- Calculates data unit volume within different type:

- Reference and lookup tables data. Assume this data is permanently stored.

- Base tables data (transaction data). Assume that this data is stored within its life cycle.

- Star schema (derived and summary). Assume that this data is stored within its life cycle.

- Calculate each type of data retention.

- Define how many months or years of each type of tables to retain.

- Calculate data growth.

- Assume that annual growth rate: applies to both transaction and reference data and data in the star schema.

- Assume that annual change rate applies only to reference data.

- Calculate Staging Area data requirements, if proposed.

Tip:

Multiply ETL volume by day by number of days held for problem resolution and re-run of transform with new extract from source systems.

- Calculate data volume for indexes, temporary tables, and transaction logs.

- Calculate the space requirement for business intelligence tools, such as cubes, and data mining.

- Consider the redo log and Oracle ASM space requirement.

- Consider the RAID architecture [RAID 1, 0+1, 5]

- Consider the backup strategy.

- Consider the compress factor if applied.

- Consider the OS and file system disk space requirements.

Formula to Determine Minimum Disk Space Requirements for an Oracle Communications Data Model Warehouse

Use the following formula, based on the factors outlined in "Calculation Factors When Making a Data Volume Calculation for an Oracle Communications Data Model Warehouse", to determine the minimum disk space requirements for an Oracle Communications Data Model warehouse.

Disk Space Minimum Requirements = Raw data size * Database space factor * (1+GrthperY)nY*OS and File system factor * Compress Factor * Storage Redundant factor

where:

- Raw data size = (reference and lookup data per year + base/transaction data per year + derived and summary data per year +staging data +other data(OLAP/Data Mining))

- Database space factor = Indexes + Temporary Tables + Logs]

- GrthperY = growth rate per year

- OS and File system factor is the install and configuration and maintain space for OS and DB

- Redundant factor= ASM disk space and RAID factor. [ RAID 1=2, RAID 5=1.25 or 1.33 ]

- Compress factor depends how you apply the compress function. If you are executing on an Exadata Database machine, it has a huge savings in disk space by using compression.

Configuring a Balanced System for Oracle Communications Data Model

Many data warehouse operations are based upon large table scans and other I/O-intensive operations, which perform vast quantities of random I/Os. In order to achieve optimal performance the hardware configuration must be sized end to end to sustain this level of throughput. This type of hardware configuration is called a balanced system. In a balanced system all components - from the CPU to the disks - are orchestrated to work together to guarantee the maximum possible I/O throughput. I/O performance is always a key consideration for data warehouse designers and administrators. The typical workload in a data warehouse is especially I/O intensive, with operations such as large data loads and index builds, creation of materialized views, and queries over large volumes of data. Design the underlying I/O system for a data warehouse to meet these heavy requirements.

To create a balanced system, answer the following questions:

- How many CPUs are required? What speed is required?

- What amount of memory is required? Data warehouse do not have the same memory requirements as mission-critical OLTP applications?

- How many I/O bandwidth components are required? What is the desired I/O speed?

Each component must be able to provide sufficient I/O bandwidth to ensure a well-balanced I/O system.

The following topics provide more information about configuring a balanced system for Oracle Communications Data Model:

- Maintaining High Throughput in an Oracle Communications Data Model Warehouse

- Configuring I/O in an Oracle Communications Data Model for Bandwidth not Capacity

- Planning for Growth of Your Oracle Communications Data Model

- Testing the I/O System Before Building the Oracle Communications Data Model Warehouse

- Balanced Hardware Configuration Guidelines for Oracle Communications Data Model

See also:

Appendix A, "Exadata and the Oracle Communications Data Model"

Maintaining High Throughput in an Oracle Communications Data Model Warehouse

The hardware configuration and data throughput requirements for a data warehouse are unique mainly because of the sheer size and volume of data. Before you begin sizing the hardware configuration for your data warehouse, estimate the highest throughput requirement to determine whether current or proposed hardware configuration can deliver the necessary performance. When estimating throughput, use the following criteria:

- The amount of data accessed by queries during peak time, and the acceptable response time

- The amount of data that is loaded within a window of time

Configuring I/O in an Oracle Communications Data Model for Bandwidth not Capacity

Based on the data volume calculated and the highest throughput requirement, you can estimate the I/O throughput along with back-end ETL process and front end business intelligence applications by time unit. Typically, a value of approximately 200MB per second I/O throughput per core is a good planning number for designing a balanced system. All subsequent critical components on the I/O path - the Host Bus Adapters, fiber channel connections, the switch, the controller, and the disks - have to be sized appropriately.

When running a data warehouse on an Oracle Real Application Cluster (Oracle RAC) it is just as important to size the cluster interconnect with the same care and caution you would use for the I/O subsystem throughput.

When configuring the storage subsystem for a data warehouse, it should be simple, efficient, highly available and very scalable. An easy way to achieve this is to apply the S.A.M.E. methodology (Stripe and Mirror Everything). S.A.M.E. can be implemented at the hardware level or by using Oracle ASM (Automatic Storage Management) or by using a combination of both. There are many variables in sizing the I/O systems, but one basic rule of thumb is that the data warehouse system has multiple disks for each CPU (at least two disks for each CPU at a bare minimum) to achieve optimal performance.

Planning for Growth of Your Oracle Communications Data Model

A data warehouse designer plans for future growth of a data warehouse. There are several approaches to handling the growth in a system, and the key consideration is to be able to grow the I/O system without compromising on the I/O bandwidth. You cannot, for example, add four disks to an existing system of 20 disks, and grow the database by adding a new tablespace striped across only the four new disks. A better solution would be to add new tablespaces striped across all 24 disks, and over time also convert the existing tablespaces striped across 20 disks to be striped across all 24 disks.

Testing the I/O System Before Building the Oracle Communications Data Model Warehouse

When creating a data warehouse on a new system, test the I/O bandwidth before creating all of the database data files to validate that the expected I/O levels are being achieved. On most operating systems, you can perform the test using simple scripts to measure the performance of reading and writing large test files.

Balanced Hardware Configuration Guidelines for Oracle Communications Data Model

You can reference the follow tips for a balanced hardware configuration:

- Total throughput = #cores X 100-200MB (depends on the chip set)

- Total host bus adaptor (HBA) throughput = Total core throughput

Note:

If total core throughput is 1.6 GB, you need four 4 Gbit HBAs.

- Use one disk controller per HBA port (throughput capacity must be equal).

- Switches must have the capacity as HBAs and disk controllers.

- Use a maximum of ten physical disk per controller (that is, use smaller drives: 146 or 300 GB).

- Use a minimum of 4 GB of memory per core (8 GB if using compress).

- Interconnect bandwidth equals I/O bandwidth (InfiniBand).

Oracle now provides the Oracle Database Machine, Exadata which combines industry-standard hardware from Oracle, Oracle Database 11g Release 2, and Oracle Exadata Storage Server Software to create a faster, more versatile database machine. It's a completely scalable and fault tolerant package for all data management, especially for data warehousing.

Oracle also has a series of Optimized Warehouse Reference configurations that help customers take the risk out of designing and deploying Oracle data warehouses. Using extensive field experience and technical knowledge, Oracle and its hardware partners have developed a choice of data warehouse reference configurations that can support various sizes, user populations and workloads. These configurations are fast, reliable and can easily scale from 500 GB to over 100 TB on single and clustered servers to support tens to thousands of users.

See:

Appendix A, "Exadata and the Oracle Communications Data Model"

C Refreshing the Data in an Oracle Communications Data Model Warehouse

In general, you manage an Oracle Communications Data Model data warehouse in much the same way that you manage any other data warehouse. Management tasks include refreshing the data in the data warehouse, optimizing the data warehouse operations, eliminating performance bottlenecks, backing up and recovering the data warehouse, and securing the data warehouse. These topics are discussed in Part IV of Oracle Database 2 Day + Data Warehousing Guide.

This appendix discusses unique considerations when refreshing the data in an Oracle Communications Data Model warehouse. It contains the following topics:

- Introduction to Refreshing the Data in an Oracle Communications Data Model Warehouse

- Refreshing Oracle Communications Data Model Relational Tables and Views

- Refreshing Oracle Communications Data Model OLAP Cubes

- Refreshing Oracle Communications Data Model Data Mining Models

Introduction to Refreshing the Data in an Oracle Communications Data Model Warehouse

"Performing an Initial Load of an Oracle Communications Data Model Warehouse" describes how to perform an intial load of an Oracle Communications Data Model data warehouse. After this initial load, you must load new data into your Oracle Communications Data Model data warehouse regularly so that it can serve its purpose of facilitating business analysis.

To load new data into your Oracle Communications Data Model warehouse, you extract the data from one or more operational systems and copy that data into the warehouse. The challenge in data warehouse environments is to integrate, rearrange and consolidate large volumes of data over many systems, thereby providing a new unified information base for business intelligence.

The successive loads and transformations must be scheduled and processed in a specific order and are determined by your business needs. Depending on the success or failure of the operation or parts of it, the result must be tracked and subsequent, alternative processes might be started.

You can do a full incremental load of the relational tables and views, OLAP cubes, and data mining models all at one time, or you can refresh the data sequentially:

1. Refreshing Oracle Communications Data Model Relational Tables and Views

2. Refreshing Oracle Communications Data Model OLAP Cubes

3. Refreshing Oracle Communications Data Model Data Mining Models

In either case, you can manage errors during the execution of the intra-ETL as described in "Managing Errors During Oracle Communications Data Model Intra-ETL Execution".

See:

Chapter 5, "ETL Implementation and Customization."

Refreshing Oracle Communications Data Model Relational Tables and Views

Refreshing the relational tables and views in an Oracle Communications Data Model is a multi-step process:

1. Refresh the reference, lookup, and base tables in the Oracle Communications Data Model warehouse with OLTP data by executing the source-ETL that you have written.

2. Update the parameters of the DWC_ETL_PARAMETER control table in the ocdm_sys schema. For an incremental load of an Oracle Communications Data Model warehouse, specify the values shown in the following table (that is, the beginning and end date of the ETL period).

Columns

Value

PROCESS_NAME

'OCDM-INTRA-ETL'

FROM_DATE_ETL

The beginning date of the ETL period.

TO_DATE_ETL

The ending date of the ETL period.

See:

Oracle Communications Data Model Reference for more information on the DWC_ETL_PARAMETER control table.

3. Refresh the derived tables and aggregate tables which are materialized views in Oracle Communications Data Model by executing the DRVD_FLOW and AGGR_FLOW subprocess of the INTRA_ETL_FLW process flow. See "Executing the Default Oracle Communications Data Model Intra-ETL " for more information.

See also:

Oracle Warehouse Builder Sources and Targets Guide

Refreshing Oracle Communications Data Model OLAP Cubes

On a scheduled basis you must update the OLAP cube data with the relational data that has been added to the Oracle Communications Data Model data warehouse since the initial load of the OLAP cubes.

You can execute the Oracle Communications Data Model ETL to update the OLAP cubes in the following ways

- Refresh all of the data in the warehouse by executing the Oracle Warehouse Builder Workflow INTRA_ETL_FLW in one of the ways that are described in "Executing the Default Oracle Communications Data Model Intra-ETL ".
The OLAP Cubes are populated through OLAP_MAP which is a part of Oracle Communications Data Model intra-ETL main workflow INTRA_ETL_FLW.

- Refresh only the OLAP cube data by executing the OLAP_MAP Oracle Warehouse Builder mapping in the Oracle Warehouse Builder control center.

Note:

You must refresh the corresponding materialized view of the OLAP cubes you are refreshing before you execute OLAP_MAP. (For the mapping between OLAP cube and materialized views, refer to Oracle Communications Data Model Reference.

Take these steps to perform an incremental load of the analytic workspace that is part of the Oracle Communications Data Model warehouse:

1. Update the aggregate tables which are materialized views in Oracle Communications Data Model. See "Refreshing Oracle Communications Data Model Relational Tables and Views" for more information.

2. Execute the intra-ETL to load the cube data in one of the ways described in "Executing the Default Oracle Communications Data Model Intra-ETL ".

3. If necessary, recover from errors that happen during the execution of OLAP_MAP take the following steps.

1. Change the value of the BUILD_METHOD column of the ocdm_sys.DWC_OLAP_ETL_PARAMETER table to "C".

2. In Oracle Warehouse Builder, rerun the OLAP_MAP map.

Refreshing Oracle Communications Data Model Data Mining Models

The MINING_FLW sub-process flow of the INTRA_ETL_FLW process flow triggers the data mining model refreshment. After the initial load of the warehouse, it is recommended to refresh the data mining models monthly. Refreshing the data models is integrated into the MINING_FLW sub-process flow. You can also manually refresh the data models.

The way you refresh a data mining model varies depending on whether you want to refresh all of the models or only one model:

- To manually refresh all mining models, call the following procedure.

ocdm_mining.PKG_OCDM_MINING.REFRESH_MODEL( MONTH_CODE,P_PROCESS_NO)

This procedure performs the following tasks for each model:

1. Refreshes the mining source materialized views based on the latest data from ocdm_sys schema.

2. Trains each model on the new training data.

3. Applies each model onto the new apply data set.

- To manually re-create only one mining model, you can call the corresponding procedure. For example, to re-create ther churn SVM model, you can call the following procedure.

ocdm_mining.create_churn_svm_model( MONTH_CODE );

"Tutorial: Customizing the Churn Prediction Mining Model" provides detailed instructions for refreshing a single data mining model.

See also:

"Troubleshooting Data Mining Model Creation"

D Working with User Privileges in Oracle Communications Data Model

This appendix provides information about managing user privileges in Oracle Communications Data Model. It contains the following topics:

- Accounts Created for Oracle Communications Data Model

- When You Must Consider User Privileges in an Oracle Communications Data Model

- Granting Only Select Privileges to Users of the Sample Reports

Accounts Created for Oracle Communications Data Model

Installing the Oracle Communications Data Model component creates two accounts: the OCDM_SYS and OCDM_MINING accounts. Installing the Oracle Communications Data Model sample reports create the OCDM_SAMPLE account. Ensure you unlock these accounts with new passwords following the post-installation steps provided in Oracle Communications Data Model Installation Guide.

See:

Oracle Communications Data Model Installation Guide for information on installing Oracle Communications Data Model and for unlocking the OCDM_SYS and OCDM_MINING accounts

The OCDM_SYS and OCDM_MINING accounts serve different purposes:

- ocdm_sys is the main schema for Oracle Communications Data Model. This schema contains all the relational and OLAP components of Oracle Communications Data Model.
The Oracle Communications Data Model data mining result tables are also in this schema. The final output from the data mining models is saved back into ocdm_sys schema for easy integration with other components. With mining result tables in ocdm_sys schema, customer can run report joining relational content with Mining results much easily. For example, customer can query for revenue sum of customers belonging to a specific segment according to Customer Segmentation model.

- ocdm_mining is the data mining schema of Oracle Communications Data Model. This schema contains all the mining components of Oracle Communications Data Model except the final result tables.

When You Must Consider User Privileges in an Oracle Communications Data Model

The installation process grants the necessary privileges required for users of both accounts. After installing the product, you only need to consider user privileges in the following situations:

- The ocdm_mining schema requires select privileges to only a small amount of tables in the ocdm_sys schema, (that is, only those tables used to hold the results of the data models shipped with Oracle Communications Data Model). If you decide to create additional data mining models that require more table access from ocdm_sys schema, grant the select privilege explicitly.

- The intra-ETL programs are executed inside the ocdm_sys schema, therefore, they require the full access to that schema. By default, the Oracle Warehouse Builder intra-ETL mappings for Oracle Communications Data Model connect to the ocdm_sys schema for intra-ETL execution.

- By default, the Oracle Communications Data Model sample reports connect to the ocdm_sys schema directly. For security reason, you may want to grant only select privileges to users of the sample reports by following the steps outlined in "Granting Only Select Privileges to Users of the Sample Reports".

Granting Only Select Privileges to Users of the Sample Reports

To grant only select privileges to users of the sample reports, take the following steps:

1. Create a dedicated reporting user (for example, OCDM_Report).

2. Grant select privilege for all Oracle Communications Data Model tables required for reporting to OCDM_Report. (The easiest way to select privileges for these tables is to grant all Oracle Communications Data Model tables that start with a prefix of DWA_, DWB_, DWD_, DWR_, or DWL_.)

3. Create a view (or synonym) in OCDM_Report schema that points to the ocdm_sys tables.

4. In the Oracle Business Intelligence Suite Enterprise Edition repository for Oracle Communications Data Model, change the connection information to point to the new schema.

E Upgrading Oracle Communications Data Model

This appendix provides information about how to upgrade Oracle Communications Data Model. It contains the following topics:

- Preparing to Upgrade Oracle Communications Data Model

- Upgrade Paths for Oracle Communications Data Model

- Regression Testing for Oracle Communications Data Model

Note:

Detailed upgrade steps and descripts are provided with each Oracle Communications Data Model upgrade package. This appendix provides general information about how to upgrade the Oracle Communications Data Model.

Preparing to Upgrade Oracle Communications Data Model

Oracle has product roadmap and lifecycle management. Also, the Oracle Communications Data Model product and engineering team oversees the specific industry trends, progress, and best practices in order to bring these features into Oracle Communications Data Model.

With the next release of Oracle Communications Data Model, identify the components in Oracle Communications Data Model that must be upgraded and analyze the impact that the upgrade will have on your existing data warehouse architecture and applications. The Oracle Communications Data Model upgrade focuses on Oracle Communications Data Model itself -- it does not include considerations of upgrading a hardware platform or related software.

When you acquire an Oracle Communications Data Model upgrade package, take the following steps to upgrade:

1. Understand upgrade purpose and process, analysis impact on your existing data warehouse system then decide whether to accept it, if accept then proceed to next steps.

2. Check the related environment requirements, such as software version requirement.

3. Backup current schemas.

4. If you previously customized Oracle Communications Data Model, prepare the archived original Oracle Communications Data Model schema; otherwise, skip this step.

5. Follow the specific upgrade path guide "Upgrade Paths for Oracle Communications Data Model".

6. List the additional upgrade tasks after Oracle upgrade scripts.

7. Make those addition changes after Oracle upgrade scripts as patch scripts.

Upgrade Paths for Oracle Communications Data Model

There ares two different path to upgrade Oracle Communications Data Model , it depends on whether Oracle Communications Data Model has been customized.

Upgrade Path A: Based on an Earlier Default Oracle Communications Data Model

Take the following steps to upgrade Oracle Communications Data Model based on an earlier default Oracle Communications Data Model:

1. Confirm to meet the requirements of upgrade scripts.

2. Execute the upgrade scripts.

3. Migrate data if necessary.

4. Additions, if necessary, to modify the:

- Derived , aggregate layer

- Source-ETL and intra-ETL mapping

- Workflow

- Business intelligence applications

- Metadata Repository

Upgrade Path B: Based on a Customized Oracle Communications Data Model

Take the following steps to upgrade your Oracle Communications Data Model based on a customized Oracle Communications Data Model:

1. Confirm to meet the requirement of upgrade scripts.

2. Restore the original Oracle Communications Data Model before upgrade.

3. Execute the Oracle upgrade scripts based on original Oracle Communications Data Model.

4. Review your customization patch and upgrade it if necessary.

5. Execute the (new) customization patch.

6. Migrate the data.

7. Additions, if necessary, to modify the:

- Derived , aggregate layer

- Source-ETL and intra-ETL mapping

- Workflow

- Business intelligence applications

- Metadata Repository

Regression Testing for Oracle Communications Data Model

After you upgrade Oracle Communications Data Model, perform regression testing. The major tasks to perform are:

- Create a test plan.

- Execute a test case.

- Test error handling.

- Create a final report.

Index

A  C  D  E  F  H  I  J  K  L  M  N  O  P  Q  R  S  T  U

A

access layer, 3.1

customizing, 4, 4.1

Oracle Communications Data Model, 3.1, 4

accounts

for Oracle Communications Data Warehouse, D.1

AGGR_FLW, 5.3.1

aggregate tables

in Oracle Communications Data Model, 4.3

refreshing, 5.3.1

As Is reports, 6.6

As Was reports, 6.6

C

Churn Prediction Data Mining Model, 4.2.2.3

compression

hybrid columnar, A.2.2

in Oracle Communications Data Model, 3.3.2, 3.3.2.1, 3.3.2.2

materialized views, 4.5.4

configuring Oracle Communications Data Model warehouse, B.2

conventions

when customizing logical model, 2.1.2

when customizing physical model, 3.2.2

cubes

adding materialized view capabilities to, 4.4.3.2

changing the dimensions of, 4.4.6

changing the measures of, 4.4.6

customizing, 4.4.4

data maintenance methods, 4.4.9

forecast, 4.4.7

in Oracle Communications Data Model, 4.4.4, 4.4.5

partitioning, 4.4.8

customizing

access layer, 4

cubes, 4.4.4

logical model, 2.1

logical model, example, 2.3

Oracle Communications Data Model, 1.2

physical data model, 3

D

dashboards, Oracle Communications Data Model, 6.2, 6.7

data governance committee, responsibilities of, 1.3.2

data mining models

customizing, 4.2.2

refreshing, 5.3.1

derived tables

in Oracle Communications Data Model, 4.2

refreshing, 5.3.1

dimensional components, Oracle Communications Data Model, 4.4

DRVD_FLW, 5.3.1

E

error handling

during intra-ETL execution, 5.3.3

ETL for Oracle Communications Data Model, 5.1

Exadata

and Oracle Communications Data Model, A

Exadata hybrid columnar compression

and Oracle Communications Data Model, 3.3.2.1, A.2.2

F

fit-gap analysis for Oracle Communications Data Model, 1.5

forecast cube in Oracle Communications Data Model, 4.4.7

foundation layer

defined, 3.1

Oracle Communications Data Model, 3.1

H

hybrid columnar compression, A.2.2

I

implementing

Oracle Communications Data Model, 1.2

implementors of Oracle Communications Data Model

prerequisite knowledge, 1.3.1

indexes

in Oracle Communications Data Model, 3.3.6

materialized views, 4.5.2

partitioning, 3.3.6

integrity constraints

in Oracle Communications Data Model, 3.3.5

INTRA_ETL_FLW process flow, 5.3.1

intra-ETL

customizing, 5.3

executing, 5.3.2.1

managing errors, 5.3.3

monitoring execution of, 5.3.3.1

Oracle Communications Data Model, 5.1

recovery, 5.3.3.2

troubleshooting, 5.3.3.3

J

join performance, improving, 3.3.7.3

K

keys, surrogate

in Oracle Communications Data Model, 3.3.4

L

loading Oracle Communications Data Model data, 5.3.2, C

logical model of Oracle Communications Data Model

common change scenarios, 2.2

customizing, 2.1, 2.1.2

customizing example, 2.3

M

materialized views

compressing, 4.5.4

in Oracle Communications Data Model, 4.5

indexing, 4.5.2

partition change tracking, 4.5.3

partitioning, 4.5.3

refresh options

refreshing, 4.5.1

Metadata Dependency Manager

with Oracle Communications Data Model, 1.4.4

metadata management

repository, 1.4.2, 1.4.3

with Oracle Communications Data Model, 1.4

metadata repository, 1.4.2

browsing, 1.4.3

with Oracle Communications Data Model, 1.4.3

MINING_FLW, 5.3.1

N

naming conventions

for logical model of Oracle Communications Data Model, 2.1.2

for physical model of Oracle Communications Data Model, 3.2.2

O

Oracle Communications Data Model

access layer, 3.1, 4

accounts for, D.1

components of, 1.1.1

customizing, 1.2

customizing logical model, 2.1, 2.1.2

customizing physical model, 3.1, 3.2, 3.2.1, 3.2.2, 3.3

dashboards, 6.2

data governance, 1.3.2

described, 1.1

dimensional components, 4.4

ETL, 5.1

fit-gap analysis, 1.5

foundation layer, 3.1

implementing, 1.2

intra-ETL, 5.1

loading, 5.3.2

Metadata Dependency Manager, 1.4.4

metadata management, 1.4

metadata repository, 1.4.2, 1.4.3

Oracle products used by, 1.1.2

Oracle Warehouse Builder, using with, 1.4.4

physical layers of, 3.1

pre-implementation tasks, 1.3

querying, 6.3

refreshing data, C, C.1

reporting, 6.1, 6.3

source-ETL, 5.1, 5.2, 5.2.1, 5.2.2, 5.2.3, 5.2.4

staging layer, 3.1

tablespaces, design recommendations, 3.3.1

upgrading, E

user privileges, D

Oracle Communications Data Model implementors

prerequisite knowledge for, 1.3.1

Oracle Communications Data Model warehouse

configuring, B.2

sizing, B.1

Oracle data mining models

Oracle Communications Data Model, 4.2.2.1

Oracle Warehouse Builder

with Oracle Communications Data Model, 1.4.4

P

parallel execution

enabling for a session, 3.3.8.1

enabling for DML operations, 3.3.8.2

in Oracle Communications Data Model, 3.3.8

partition change tracking, 4.5.3

partition exchange load, 5.2.6.5

partitioned indexes in Oracle Communications Data Model, 3.3.6

partitioning

and hybrid columnar compression, A.2.2

cubes, 4.4.8

for easier data access, 3.3.7.2

for join performance, 3.3.7.3

for manageability, 3.3.7.1

for source-ETL, 5.2.6.5

indexes, 3.3.6

materialized views, 4.5.3

tables, 3.3.7

partitions, changing, 3.3.1

physical model of Oracle Communications Data Model

characteristics of, 3.1, 3.2, 3.2.1

customizing, 3.2.2

general recommendations for, 3.3

Q

querying Oracle Communications Data Model, 6.3

R

refreshing

aggregate tables, 5.3.1

data mining models, 5.3.1

derived tables, 5.3.1

Oracle Communications Data Warehouse, C, C.1

reporting

Oracle Communications Data Model, 6.1, 6.3

reports

approaches to, 6.1

As Is, 6.6

As Was, 6.6

troubleshooting performance, 6.5

reports, Oracle Communications Data Model

creating new, 6.8

S

sample reports

customizing

Oracle Communications Data Model, 6.2

sizing

Oracle Communications Data Model warehouse, B.1

source-ETL

exception handling, 5.2.5

jobs control, 5.2.4

loading considerations, 5.2.6

Oracle Communications Data Model, 5.1, 5.2, 5.2.1, 5.2.2, 5.2.3, 5.2.4, 5.2.6

parallel direct path load, 5.2.6.4

partitioning for, 5.2.6.5

workflow, 5.2.4

staging layer, 3.1

Oracle Communications Data Model, 3.1

star queries, optimizing, 6.4

subtypes

defining tables for, 3.3.3

physical implementation of, 3.3.3

supertypes

defining tables for, 3.3.3

physical implementation of, 3.3.3

surrogate keys

in Oracle Communications Data Model, 3.3.4

T

tables

aggregate, 4.3

compressing, 3.3.2, 3.3.2.1, 3.3.2.2

derived, 4.2

partitioning, 3.3.7

tablespace in Oracle Communications Data Model, 3.3.1

U

upgrading Oracle Communications Data Warehouse, E

user privileges in Oracle Communications Data Warehouse, D

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