Data Warehouse Usage Types & Motivation for OLAM

(Same underlying question as the 2022 paper — see below for the full breakdown.)

Three Types of Data Warehouse Usage

• Information processing answers known questions with simple queries/reports.
• Analytical processing (OLAP) lets users interactively explore a multidimensional cube.
• Data mining automatically discovers patterns the user didn't know to ask about.

Motivation for OLAP Mining (OLAM)

OLAM integrates OLAP with data mining, applying mining directly on multidimensional cubes instead of raw flat data:

1. High-quality warehouse data — already cleaned/integrated, giving more reliable mining results than mining raw operational data.
2. Reusable infrastructure — ETL, metadata, and OLAP engines built for the warehouse serve mining too, avoiding duplicated effort.
3. OLAP-based exploratory analysis — analysts drill/roll/slice/dice to narrow down interesting regions before mining, far more efficient than mining an entire raw dataset.
4. Flexible, on-line mining function selection — users can switch between classification, association, clustering, etc. at different cube cells/granularities within the same interactive session.

        Data Warehouse (Cube)
               │
     ┌─────────┼─────────┐
     ▼         ▼         ▼
   OLAP     OLAM       Mining
 (explore) (integrated) (patterns)
     └─────────┴─────────┘

In short: OLAM combines OLAP's interactive navigation with mining's automatic pattern discovery, letting analysts first zoom into the relevant cube region, then mine it for deeper insight.