Introduces Oracle Machine Learning for SQL to perform a variety of machine learning tasks.
2.1 About Oracle Machine Learning for SQL
Understand the uses of Oracle Machine Learning for SQL and learn about different machine learning techniques.
OML4SQL provides a powerful, state-of-the-art machine learning capability within Oracle Database. You can use OML4SQL to build and deploy predictive and descriptive machine learning applications, to add intelligent capabilities to existing applications, and to generate predictive queries for data exploration.
OML4SQL offers a broad set of in-database algorithms for performing a variety of machine learning tasks, such as classification, regression, anomaly detection, feature extraction, clustering, and market basket analysis. The algorithms can work on standard case data, transactional data, star schemas, and unstructured text data. OML4SQL is uniquely suited to the analysis of very large data sets.
Oracle Machine Learning for SQL, along with Oracle Machine Learning for R and Oracle Machine Learning for Python, is a component of Oracle Machine Learning that provides three powerful APIs for in-database machine learning, among other features.
2.2 Oracle Machine Learning for SQL in the Database Kernel
Learn about the implementation of Oracle Machine Learning for SQL (OML4SQL) in Oracle Database kernel and its advantages.
OML4SQL is implemented in the Oracle Database kernel. OML4SQL models are first class database objects. Oracle Machine Learning for SQL processes use built-in features of Oracle Database to maximize scalability and make efficient use of system resources.
OML4SQL within Oracle Database offers many advantages:
No Data Movement: Some machine learning products require that the data be exported from a corporate database and converted to a specialized format. With OML4SQL, no data movement or conversion is needed. This makes the entire process less complex, time-consuming, and error-prone, and it allows for the analysis of very large data sets.
Security: Your data is protected by the extensive security mechanisms of Oracle Database. Moreover, specific database privileges are needed for different machine learning activities. Only users with the appropriate privileges can define, manipulate, or apply machine learning model objects.
Data Preparation and Administration: Most data must be cleansed, filtered, normalized, sampled, and transformed in various ways before it can be mined. Up to 80% of the effort in a machine learning project is often devoted to data preparation. OML4SQL can automatically manage key steps in the data preparation process. Additionally, Oracle Database provides extensive administrative tools for preparing and managing data.
Ease of Data Refresh: Machine learning processes within Oracle Database have ready access to refreshed data. OML4SQL can easily deliver machine learning results based on current data, thereby maximizing its timeliness and relevance.
Oracle Database Analytics: Oracle Database offers many features for advanced analytics and business intelligence. You can easily integrate machine learning with other analytical features of the database, such as statistical analysis and OLAP.
Oracle Technology Stack: You can take advantage of all aspects of Oracle's technology stack to integrate machine learning within a larger framework for business intelligence or scientific inquiry.
Domain Environment: Machine learning models have to be built, tested, validated, managed, and deployed in their appropriate application domain environments. Machine learning results may need to be post-processed as part of domain specific computations (for example, calculating estimated risks and response probabilities) and then stored into permanent repositories or data warehouses. With OML4SQL, the pre- and post-machine learning activities can all be accomplished within the same environment.
Application Programming Interfaces: The PL/SQL API and SQL language operators provide direct access to OML4SQL functionality in Oracle Database.
2.3 Oracle Machine Learning for SQL in Oracle Exadata
Understand how complex scoring and algorithmic processing is done using Oracle Exadata.
Scoring refers to the process of applying a OML4SQL model to data to generate predictions. The scoring process may require significant system resources. Vast amounts of data may be involved, and algorithmic processing may be very complex.
With OML4SQL, scoring can be off-loaded to intelligent Oracle Exadata Storage Servers where processing is extremely performant.
Oracle Exadata Storage Servers combine Oracle's smart storage software and Oracle's industry-standard hardware to deliver the industry's highest database storage performance. For more information about Oracle Exadata, visit the Oracle Technology Network.
2.4 About Partitioned Models
Introduces partitioned models to organize and represent multiple models.
When you build a model on your data set and apply it to new data, sometimes the prediction may be generic that performs badly when run on new and evolving data. To overcome this, the data set can be divided into different parts based on some characteristics. Oracle Machine Learning for SQL supports partitioned model. Partitioned models allow users to build a type of ensemble model for each data partition. The top-level model has sub models that are automatically produced. The sub models are based on the attribute options. For example, if your data set has an attribute called MARITAL with four values and you have defined it as the partitioned attribute. Then, four sub models are created for this attribute. The sub models are automatically managed and used as a single model. The partitioned model automates a typical machine learning task and can potentially achieve better accuracy through multiple targeted models.
The partitioned model and its sub models reside as first class, persistent database objects. Persistent means that the partitioned model has an on-disk representation.
To create a partitioned model, include the
ODMS_PARTITION_COLUMNS setting. To define the number of partitions, include the
ODMS_MAX_PARTITIONS setting. When you are making predictions, you must use the top-level model. The correct sub model is selected automatically based on the attribute, the attribute options, and the partition setting. You must include the partition columns as part of the
USING clause when scoring. The
GROUPING hint is an optional hint that applies to machine learning scoring functions when scoring partitioned models.
The partition names, key values, and the structure of the partitioned model are available in the
2.5 Interfaces to Oracle Machine Learning for SQL
Introduces supported interfaces for Oracle Machine Learning for SQL.
The programmatic interfaces to Oracle Machine Learning for SQL are PL/SQL for building and maintaining models and a family of SQL functions for scoring. OML4SQL also supports a graphical user interface, which is implemented as an extension to Oracle SQL Developer.
Oracle Predictive Analytics, a set of simplified OML4SQL routines, is built on top of OML4SQL and is implemented as a PL/SQL package.
2.5.1 PL/SQL API
Includes PL/SQL package for Oracle Machine Learning for SQL.
The OML4SQL PL/SQL API is implemented in the
DBMS_DATA_MINING PL/SQL package, which contains routines for building, testing, and maintaining machine learning models. A batch apply operation is also included in this package.
The following example shows part of a simple PL/SQL script for creating an SVM classification model called SVMC_SH_Clas_sample. The model build uses weights, specified in a weights table, and settings, specified in a settings table. The weights influence the weighting of target classes. The settings override default behavior. The model uses Automatic Data Preparation (
prep_auto_on setting). The model is trained on the data in mining_data_build_v.
Example 2-1 Creating a Classification Model
----------------------- CREATE AND POPULATE A CLASS WEIGHTS TABLE ------------ CREATE TABLE svmc_sh_sample_class_wt ( target_value NUMBER, class_weight NUMBER); INSERT INTO svmc_sh_sample_class_wt VALUES (0,0.35); INSERT INTO svmc_sh_sample_class_wt VALUES (1,0.65); COMMIT; ----------------------- CREATE AND POPULATE A SETTINGS TABLE ------------------ CREATE TABLE svmc_sh_sample_settings ( setting_name VARCHAR2(30), setting_value VARCHAR2(4000)); BEGIN INSERT INTO svmc_sh_sample_settings (setting_name, setting_value) VALUES (dbms_data_mining.algo_name, dbms_data_mining.algo_support_vector_machines); INSERT INTO svmc_sh_sample_settings (setting_name, setting_value) VALUES (dbms_data_mining.svms_kernel_function, dbms_data_mining.svms_linear); INSERT INTO svmc_sh_sample_settings (setting_name, setting_value) VALUES (dbms_data_mining.clas_weights_table_name, 'svmc_sh_sample_class_wt'); INSERT INTO svmc_sh_sample_settings (setting_name, setting_value) VALUES (dbms_data_mining.prep_auto, dbms_data_mining.prep_auto_on); END; / ------------------------ CREATE THE MODEL ------------------------------------- BEGIN DBMS_DATA_MINING.CREATE_MODEL( model_name => 'SVMC_SH_Clas_sample', mining_function => dbms_data_mining.classification, data_table_name => 'mining_data_build_v', case_id_column_name => 'cust_id', target_column_name => 'affinity_card', settings_table_name => 'svmc_sh_sample_settings'); END; /
2.5.2 SQL Functions
Oracle Machine Learning for SQL supports SQL functions for performing prediction, clustering, and feature extraction.
The functions score data by applying an OML4SQL model object or by running an analytic clause that performs dynamic scoring.
The following example shows a query that applies the classification model
svmc_sh_clas_sample to the data in the view
mining_data_apply_v. The query returns the average age of customers who are likely to use an affinity card. The results are broken out by gender.
Example 2-2 The PREDICTION Function
SELECT cust_gender, COUNT(*) AS cnt, ROUND(AVG(age)) AS avg_age FROM mining_data_apply_v WHERE PREDICTION(svmc_sh_clas_sample USING *) = 1 GROUP BY cust_gender ORDER BY cust_gender; C CNT AVG_AGE - ---------- ---------- F 59 41 M 409 45
2.5.3 Oracle Data Miner
Oracle Machine Learning for SQL supports a graphical interface called Oracle Data Miner.
Oracle Data Miner uses a work flow paradigm to capture, document, and automate the process of building, evaluating, and applying OML4SQL models. Within a work flow, you can specify data transformations, build and evaluate multiple models, and score multiple data sets. You can then save work flows and share them with other users.
Figure 2-1 An Oracle Data Miner Workflow
Description of "Figure 2-1 An Oracle Data Miner Workflow"
For information about Oracle Data Miner, including installation instructions, visit Oracle Technology Network.
2.5.4 Predictive Analytics
Predictive analytics is a technology that captures Oracle Machine Learning for SQL processes in simple routines.
Predictive analytics uses OML4SQL technology, but knowledge of OML4SQL is not needed to use predictive analytics. You can use predictive analytics by specifying an operation to perform on your data. You do not need to create or use OML4SQL models or understand the OML4SQL functions and algorithms summarized in "Oracle Machine Learning for SQL Basics ".
Oracle Machine Learning for SQL predictive analytics operations are described in the following table:
Table 2-1 Oracle Predictive Analytics Operations
Explains how individual predictors (columns) affect the variation of values in a target column
For each case (row), predicts the values in a target column
Creates a set of rules for cases (rows) that imply the same target value
The Oracle predictive analytics operations are implemented in the
DBMS_PREDICTIVE_ANALYTICS PL/SQL package. They are also available in Oracle Data Miner.
2.6 Overview of Database Analytics
An overview of native analytics supported by Oracle Database.
Oracle Database supports an array of native analytical features. Since all these features are part of a common server it is possible to combine them efficiently. The results of analytical processing can be integrated with Oracle Business Intelligence Suite Enterprise Edition and other BI tools and applications.
The possibilities for combining different analytics are virtually limitless. Example 2-3 shows Oracle Machine Learning for SQL and text processing within a single SQL query. The query selects all customers who have a high propensity to attrite (> 80% chance), are valuable customers (customer value rating > 90), and have had a recent conversation with customer services regarding a Checking Plus account. The propensity to attrite information is computed using a OML4SQL model called
tree_model. The query uses the Oracle Text
CONTAINS operator to search call center notes for references to Checking Plus accounts.
Some of the native analytics supported by Oracle Database are described in the following table:
Table 2-2 Oracle Database Native Analytics
Example 2-3 SQL Query Combining Oracle Machine Learning for SQL and Oracle Text
SELECT A.cust_name, A.contact_info FROM customers A WHERE PREDICTION_PROBABILITY(tree_model, 'attrite' USING A.*) > 0.8 AND A.cust_value > 90 AND A.cust_id IN (SELECT B.cust_id FROM call_center B WHERE B.call_date BETWEEN '01-Jan-2005' AND '30-Jun-2005' AND CONTAINS(B.notes, 'Checking Plus', 1) > 0);