Oracle Data Mining and Analytics

The following papers are available on OTN (link to site):

• Adding Data Mining to Extend Your OLAP BI Solution (link)
• Data-Centric Automated Data Mining (link)
• Mining High-Dimensional Data for Information Fusion (link)
• Support Vector Machines in Oracle Database 10g (link)
• Data Mining-Based Intrusion Detection (link)
• Oracle9i O-Cluster: Scalable Clustering of Large High Dimensional Data Sets (link)
• Clustering Large Databases with Numeric and Nominal Values Using Orthogonal Projections (link)

Presentations on some of these papers can also be found on the above OTN site.

The first paper shows how data mining can be leveraged to select relevant dimensions for creating OLAP cubes.

The second paper proposes a new approach to the design of data mining applications targeted at database and business intelligence users. This approach uses a data-centric focus and automated methodologies to make data mining accessible to non-experts.

The third paper shows how the RDMBS provides an effective platform for building information fusion applications. It demonstrates the approach on satellite imagery using a combination of data mining and spatial processing components.

The fourth paper presents Oracle’s implementation of SVM where the primary focus lies on ease of use and scalability while maintaining high performance accuracy.

The fifth paper introduces DAID, a database-centric architecture that leverages data mining within the Oracle RDBMS to address the challenges that exist in the design and implementation of production quality intrusion detection systems. DAID also offers numerous advantages in terms of scheduling capabilities, alert infrastructure, data analysis tools, security, scalability, and reliability.

The last two papers describes O-Cluster, a clustering algorithm part of Oracle Data Mining that can scale to large number of attributes and rows.

Readings: Business intelligence, Data mining, Oracle analytics