Data Crunching

Spatial Data Mining Data mining is the automated process of discovering patterns in data.
The purpose is to find correlation among different datasets that are unexpected.
Supermarket chains are a prime example of entities that use data mining techniques in an effort to increase sales by trying to find correlations in consumer buying practices.
In a hypothetical situation, a data miner might find a pattern that people who purchase high-end cat food also are strong purchasers of floor wax.
As a result of this analysis, the supermarket might then place the pet food products in the same aisle as the household cleaners in an attempt to induce higher sales.
On-Line Transaction Processing (OLTP) is the tradional model for enterprise data processing.
In OLTP, the emphasis is on transactions involving the input, update, and retrieval of data. On-Line Analytical Processing (OLAP) applications query the database to collate, summarize, and analyze its contents.
Data mining augments the OLAP process by applying artificial intelligence and machine learning techniques to find previously unknown or undiscovered relationships in the data.

This is different from analytical techniques in which the goal is to prove or disprove an existing hypothesis.

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