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Data Warehousing with Data Marts

bulletDSS - Decision Support System Data Vs. Operational Data         
bullet OLAP - Multidimensional data Vs. Relational data analysis                         
bullet ROLAP Vs. MOLAP analysis with Drill Down and Roll Up                           
bulletDatabase Schema Design - Star Schema
bullet ETL - Extraction, Transformation and Loading 
bulletData Integration and Validation
bulletOracle 9i Vs. SQL2000



Data Mining

AData Preparation

The main data sets used by our data-mining operation are identified and cleansed for any data impurities. Since the data in the data warehouse are integrated and filtered, the data warehouse is the target set for our data-mining operations.

BData Analysis and Classification

We study the data and identify the common data characteristics or patterns in order to apply the specific algorithms to find:

  1. Data groupings, classifications, clusters, or sequences.
  2. Data dependencies, links, or relationships.
  3. Data patterns, trends, and deviations.

C.  Knowledge Acquisition

During this phase, we select the appropriate modeling or knowledge acquisition algorithms. The most typical algorithms used in data mining are based on neural networks, decision trees, rules induction, genetic algorithms, classification and regression trees, memory-based reasoning, nearest neighbor and data visualization. We use many of these algorithms in any combination to generate a computer model that reflects the behavior of the target data set.

D.  Prognosis

The data mining findings are used to predict future behavior and forecast business outcomes.                                                                                                                                             



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