Release 1 (9.0.1)
Part Number A90237-01
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| Oracle9i Data Warehousing Guide Release 1 (9.0.1) Part Number A90237-01 |
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Describes a fact (or measure) that can be summarized through addition. An additive fact is the most common type of fact. Examples include Sales, Cost, and Profit. Contrast with nonadditive, semi-additive.
The Summary Advisor recommends which materialized views to retain, create, and drop. It helps database administrators manage materialized views. It is a GUI in Oracle Enterprise Manager, and has similar capabilities to the DBMS_OLAP package.
A descriptive characteristic of one or more levels. Attributes represent logical groupings that enable end users to select data based on like characteristics. Note that in relational modeling, an attribute is defined as a characteristic of an entity. In Oracle9i, an attribute is a column in a dimension that characterizes elements of a single level.
The process of consolidating data values into a single value. For example, sales data could be collected on a daily basis and then be aggregated to the week level, the week data could be aggregated to the month level, and so on. The data can then be referred to as aggregate data. Aggregation is synonymous with summarization, and aggregate data is synonymous with summary data.
Summarized data. For example, unit sales of a particular product could be aggregated by day, month, quarter and yearly sales.
A value at any level above a given value in a hierarchy. For example, in a Time dimension, the value 1999 might be the ancestor of the values Q1-99 and Jan-99. See also descendant, hierarchy, level.
A descriptive characteristic of one or more levels. For example, the Product dimension for a clothing manufacturer might contain a level called Item, one of whose attributes is Color. Attributes represent logical groupings that enable end users to select data based on like characteristics.
Note that in relational modeling, an attribute is defined as a characteristic of an entity. In Oracle9i, an attribute is a column in a dimension that characterizes elements of a single level.
A value at the level below a given value in a hierarchy. For example, in a Time dimension, the value Jan-99 might be the child of the value Q1-99. A value can be a child for more than one parent if the child value belongs to multiple hierarchies. See also hierarchy, level, parent.
The process of resolving inconsistencies and fixing the anomalies in source data, typically as part of the ETL process. See also ETL.
A repository standard used by Oracle data warehousing, decision support, and OLAP tools including Oracle Warehouse Builder. The CWM repository schema is a standalone product that other products can share--each product owns only the objects within the CWM repository that it creates.
A procedure for combining the elements in multiple sets. For example, given two columns, each element of the first column is matched with every element of the second column. A simple example is shown below:
Col1 Col2 Cross Product ---- ---- ------------- a c ac b d ad bc bd
Cross products are performed when grouping sets are concatenated, as described in Chapter 18, "SQL for Aggregation in Data Warehouses".
A database, application, repository, or file that contributes data to a warehouse.
A data warehouse that is designed for a particular line of business, such as sales, marketing, or finance. In a dependent data mart, the data can be derived from an enterprise-wide data warehouse. In an independent data mart, data can be collected directly from sources. See also data warehouse.
A relational database that is designed for query and analysis rather than transaction processing. A data warehouse usually contains historical data that is derived from transaction data, but it can include data from other sources. It separates analysis workload from transaction workload and enables a business to consolidate data from several sources.
In addition to a relational database, a data warehouse environment often consists of an ETL solution, an OLAP engine, client analysis tools, and other applications that manage the process of gathering data and delivering it to business users. See also ETL, OLAP.
The process of allowing redundancy in a table so that it can remain flat. Contrast with normalize.
A fact (or measure) that is generated from existing data using a mathematical operation or a data transformation. Examples include averages, totals, percentages, and differences.
A structure, often composed of one or more hierarchies, that categorizes data. Several distinct dimensions, combined with measures, enable end users to answer business questions. Commonly used dimensions are Customer, Product, and Time. In Oracle 9i, a dimension is a database object that defines hierarchical (parent/child) relationships between pairs of column sets. In Oracle Express, a dimension is a database object that consists of a list of values.
One element in the list that makes up a dimension. For example, a computer company might have dimension values in the Product dimension called LAPPC and DESKPC. Values in the Geography dimension might include Boston and Paris. Values in the Time dimension might include MAY96 and JAN97.
To navigate from one item to a set of related items. Drilling typically involves navigating up and down through the levels in a hierarchy. When selecting data, you can expand or collapse a hierarchy by drilling down or up in it, respectively. See also drill down, drill up.
To expand the view to include child values that are associated with parent values in the hierarchy. (See also drill, drill up.)
To collapse the list of descendant values that are associated with a parent value in the hierarchy.
An object or process. For example, a dimension is an object, a mapping is a process, and both are elements.
Extraction, transformation, and loading. ETL refers to the methods involved in accessing and manipulating source data and loading it into a data warehouse. The order in which these processes are performed varies.
Note that ETT (extraction, transformation, transportation) and ETM (extraction, transformation, move) are sometimes used instead of ETL. (See also data warehouse, extraction, transformation, transportation.)
The process of taking data out of a source as part of an initial phase of ETL. (See also ETL.)
A table in a star schema that contains facts. A fact table typically has two types of columns: those that contain facts and those that are foreign keys to dimension tables. The primary key of a fact table is usually a composite key that is made up of all of its foreign keys.
A fact table might contain either detail level facts or facts that have been aggregated (fact tables that contain aggregated facts are often instead called summary tables). A fact table usually contains facts with the same level of aggregation.
Data, usually numeric and additive, that can be examined and analyzed. Values for facts or measures are usually not known in advance; they are observed and stored. Examples include Sales, Cost, and Profit. Fact and measure are synonymous; fact is more commonly used with relational environments, measure is more commonly used with multidimensional environments. See also derived fact.
An operation that applies only the data changes to a materialized view, thus eliminating the need to rebuild the materialized view from scratch.
Maps data from flat files to tables in the warehouse.
A logical structure that uses ordered levels as a means of organizing data. A hierarchy can be used to define data aggregation; for example, in a Time dimension, a hierarchy might be used to aggregate data from the Month level to the Quarter level to the Year level. A hierarchy can also be used to define a navigational drill path, regardless of whether the levels in the hierarchy represent aggregated totals. See also dimension, level.
The metadata container for process data.
A position in a hierarchy. For example, a Time dimension might have a hierarchy that represents data at the Month, Quarter, and Year levels.
(See also hierarchy.)
A database table that stores the values or data for the levels you created as part of your dimensions and hierarchies.
The definition of the relationship and data flow between source and target objects.
A pre-computed table comprising aggregated and/or joined data from fact and possibly dimension tables. Also known as a summary or aggregate table.
Data that describes data and other structures, such as objects, business rules, and processes. For example, the schema design of a data warehouse is typically stored in a repository as metadata, which is used to generate scripts used to build and populate the data warehouse. A repository contains metadata.
Examples include: for data, the definition of a source to target transformation that is used to generate and populate the data warehouse; for information, definitions of tables, columns and associations that are stored inside a relational modeling tool; for business rules, discount by 10 percent after selling 1,000 items.
An object that represents something to be made. A representative style, plan, or design. Metadata that defines the structure of the data warehouse.
Describes a fact (or measure) that cannot be summarized through addition. An example includes Average. Contrast with additive, semi-additive.
In a relational database, the process of removing redundancy in data by separating the data into multiple tables. Contrast with denormalize.
The process of removing redundancy in data by separating the data into multiple tables.
The cleaned, transformed data from a particular source database.
Online analytical processing. OLAP functionality is characterized by dynamic, multidimensional analysis of historical data, which supports activities such as the following:
OLAP tools can run against a multidimensional database or interact directly with a relational database.
A value at the level above a given value in a hierarchy. For example, in a Time dimension, the value Q1-99 might be the parent of the value Jan-99. See also child, hierarchy, level.
The mechanism whereby materialized views are populated with data.
A collection of related database objects. Relational schemas are grouped by database user ID and include tables, views, and other objects. See also snowflake schema, star schema. Whenever possible, a demo schema called Sales History is used throughout this Guide.
Describes a fact (or measure) that can be summarized through addition along some, but not all, dimensions. Examples include Headcount and On Hand Stock. Contrast with additive, nonadditive.
A type of star schema in which the dimension tables are partly or fully normalized. See also schema, star schema.
A database, application, file, or other storage facility from which the data in a data warehouse is derived.
A relational schema whose design represents a multidimensional data model. The star schema consists of one or more fact tables and one or more dimension tables that are related through foreign keys. See also schema, snowflake schema.
A classification system that represents or distinguishes parts of an organization or areas of knowledge. A data mart is often developed to support a subject area such as sales, marketing, or geography. See also data mart.
A layout of data in columns.
Holds the intermediate or final results of any part of the ETL process. The target of the entire ETL process is the data warehouse. See also data warehouse, ETL.
The process of manipulating data. Any manipulation beyond copying is a transformation. Examples include cleansing, aggregating, and integrating data from multiple sources.
The process of moving copied or transformed data from a source to a data warehouse. See also transformation.
The process of verifying metadata definitions and configuration parameters.
The ability to create new versions of a data warehouse project for new requirements and changes.
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