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
|
|
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 |
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
|
|
Linear algebra |
The |
|
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. |
|
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. |
|
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. |
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);