Data Warehouse Usage Types & Motivation for OLAM

Three Types of Data Warehouse Usage

• Information processing = basic querying/reporting — answers known questions.
• Analytical processing (OLAP) = interactive multidimensional exploration — user still drives the analysis by choosing what to slice/dice.
• Data mining = automated discovery — finds patterns the user didn't know to ask about (association rules, clusters, classifiers, outliers).

Motivation for OLAP Mining (OLAM)

OLAM (On-Line Analytical Mining) integrates OLAP with data mining, applying mining techniques directly on multidimensional data cubes rather than flat/raw data. Motivations:

1. High quality of data in warehouses — DW data is already cleaned, integrated, and consistent, so mining results are more reliable than mining raw operational data directly.
2. Available information processing infrastructure — ETL, metadata, indexing, and OLAP engines built for the warehouse can be reused for mining, avoiding duplicate infrastructure.
3. OLAP-based exploratory data analysis — users can interactively drill/roll/slice/dice to first narrow down interesting regions of data, then apply mining algorithms only where needed — much more efficient than mining the whole raw dataset blindly.
4. On-line selection of mining functions — integrating mining with OLAP navigation lets users flexibly switch between different mining tasks (classification, association, clustering) at different cube cells/granularities during the same interactive session.

        Data Warehouse (Cube)
               │
     ┌─────────┼─────────┐
     ▼         ▼         ▼
   OLAP     OLAM       Mining
 (explore) (integrated) (patterns)
     └─────────┴─────────┘
        Combined interactive,
        multi-level analysis

In short: OLAM lets analysts interactively navigate to the right subset of data using OLAP, then mine that subset for deeper patterns — combining the strengths of both technologies instead of treating them as separate tools.