Overview of Database Analytics

Oracle AI Database supports native analytical features. Since all these features are on a common server, they can be combined efficiently. Analytical results can be integrated with Oracle Business Intelligence Suite Enterprise Edition and other BI tools.

The possibilities for combining different analytics are virtually limitless. Example 7-5 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.

The following table shows some of the built-in analytics that Oracle AI Database can do:

Table 7-15 Oracle AI Database Native Analytics

Analytical Feature	Description	Documented In...
Complex data transformations	Data transformation is a key aspect of analytical applications and ETL (extract, transform, and load). You can use SQL expressions to implement data transformations, or you can use the `DBMS_DATA_MINING_TRANSFORM` package. `DBMS_DATA_MINING_TRANSFORM` is a flexible data transformation package that includes a variety of missing value and outlier treatments, as well as binning and normalization capabilities.	Oracle AI Database PL/SQL Packages and Types Reference
Statistical functions	Oracle AI Database provides a long list of SQL statistical functions with support for: hypothesis testing (such as t-test, F-test), correlation computation (such as pearson correlation), cross-tab statistics, and descriptive statistics (such as median and mode). The `DBMS_STAT_FUNCS` package adds distribution fitting procedures and a summary procedure that returns descriptive statistics for a column.	Oracle AI Database SQL Language Reference and Oracle AI Database PL/SQL Packages and Types Reference
Window and analytic SQL functions	Oracle AI Database supports analytic and windowing functions for computing cumulative, moving, and centered aggregates. With windowing aggregate functions, you can calculate moving and cumulative versions of `SUM`, `AVERAGE`, `COUNT`, `MAX`, `MIN`, and many more functions.	Oracle AI Database Data Warehousing Guide
Linear algebra	The `UTL_NLA` package exposes a subset of the popular `BLAS` and `LAPACK` (Version 3.0) libraries for operations on vectors and matrices represented as VARRAYs. This package includes procedures to solve systems of linear equations, invert matrices, and compute eigenvalues and eigenvectors.	Oracle AI Database PL/SQL Packages and Types Reference
Analytic views	Analytic views organize data using a dimensional model. They enable you to easily add aggregations and calculations to data sets and to present data in views that can be queried with relatively simple SQL.	Oracle AI Database Data Warehousing Guide
Spatial analytics	Oracle Spatial provides advanced spatial features to support high-end GIS and LBS solutions. Oracle Spatial's analysis and machine learning capabilities include functions for binning, detection of regional patterns, spatial correlation, colocation machine learning, and spatial clustering. Oracle Spatial also includes support for topology and network data models and analytics. The topology data model of Oracle Spatial allows one to work with data about nodes, edges, and faces in a topology. It includes network analysis functions for computing shortest path, minimum cost spanning tree, nearest-neighbors analysis, traveling salesman problem, among others.	Oracle Spatial Developer's Guide
Graph	The Property Graph delivers advanced graph query and analytics capabilities in Oracle AI Database. The in-memory graph server (PGX) provides a machine learning library, which supports graph-empowered machine learning algorithms. The machine learning library supports DeepWalk, supervised GraphWise, and Pg2vec algorithms.	Oracle AI Database Graph Developer's Guide for Property Graph
Text Analysis	Oracle Text uses standard SQL to index, search, and analyze text and documents stored in the Oracle AI Database, in files, and on the web. Oracle Text also supports automatic classification and clustering of document collections. Many of the analytical features of Oracle Text are layered on top of Oracle Machine Learning functionality.	Oracle Text Application Developer's Guide

Example 7-5 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);