Ramkumar Krishnan

Oracle Data Mining

Oracle has completed a major research and development effort to add native Data Mining and pattern recognition algorithms to the Oracle RDBMS. As a result, Oracle Data Mining (ODM) provides a comprehensive collection of Data Mining analytics as part of the Oracle database environment that supports the development, integration and deployment of Data Mining applications. This Data Mining infrastructure has a native SQL and PL/SQL API but can also be accessed from a Java API or the ODM user interface. ODM enables data analysts and developers to discover insights hidden in their data and create advanced Data Mining applications that extend the benefits of DMa Mining to many users throughout an organization. ODM leverages the powerful and feature-rich Oracle RDBMS environment including comprehensive capabilities for data storage, data preparation and processing, information retrieval, scalability, security, transaction control, parallelism, versioning, workflow, and reliability. In this article, we describe the functionality and algorithms behind ODM and the advantages of the Data Mining in the database paradigm. We conclude with two examples of the use of ODM: a SVM methodology for tumor classification and the integration of Naive Bayes predictive models in Oracle’s marketing business application (Oracle Marketing).

The importance of extensible database systems for e-commerce

Over the last decade, database system products have been extended to provide support for defining, storing, updating, indexing and retrieving complex data with full transaction semantics. Oracle, IBM, Informix and others have used extensibility technology to build database system extensions for text, image, spatial, audio/video, chemical, genetic and other types of complex data. Currently, we find database systems being deployed in support of e-commerce. In many cases, these e-commerce database applications use only simple SQL data types to represent items such as office supplies, computers, books and CDs. There is also a large and important set of e-commerce applications that employ complex data formats such as EDI, SWIFT and HL7. The database extensibility features initially developed to support text, spatial and similar forms of complex data are now being used to build e-commerce applications. Thus, database extensibility technology is evolving into an important mechanism to enable the development of e-commerce systems.

B/sup +/-tree indexes with hybrid row identifiers in Oracle8i

Most commercial database systems support B/sup +/-tree indexes using either: physical row identifiers, for example, DB2; or logical row identifiers, for example, NonStop SQL. Physical row identifiers provide fast access to data. However, unlike logical row identifiers, they need to be updated whenever the row moves. This paper describes an alternate approach where hybrid row identifiers are used. A hybrid row identifier consists of two components: a logical component, namely, the primary key of the base table row; and a physical component, namely, the database block address (DBA) of the row. By treating the DBA as a guess regarding where the row may be found, performance comparable to physical B/sup +/-tree indexes is attained for valid guess-DBAs. This scheme retains the logical index advantage of avoiding an immediate index update when the base table row moves. Instead, an online utility can be used to lazily fix the invalid guess-DBAs. This scheme has been used to implement B/sup +/-tree indexes for Oracle8i index-organized tables (primary B/sup +/-tree like structure) which encounter both row movement and table reorganization.