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Database System and Data Warehouse, The Compelling Need for data warehousing and Data marts in data warehousing
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Components such as Data sources, Data Staging , data loading , three tier architecture such as Data, Application layer and Metadata in the data warehouse.
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OLAP implementation considerations. Query and Reporting, Executive Information Systems (EIS), Data Warehouse and Business Strategy.
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Dimensional Modeling: Multidimensional Data Model, Schemas: the STAR schema, the Snowflake schema, fact constellation schema.
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OLAP models, overview of variations, the MOLAP model, the ROLAP model, the DOLAP model, ROLAP versus MOLAP data warehousing and data mining
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OLAP Operations Drill-down and roll-up, slice-and-dice, pivot or rotation with examples | data warehouse and data mining
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What kind of patterns to be mined, Which technologies are used, What kinds of applications are targeted, Major issues in data mining.
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Data pre-processing, data cleaning, data integration, data reduction, data transformation and data discretization, IRIS datasets. and apriori algorithm.
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Structured data Unstructured data Semi structured data Time series data Spatial data Web data Biological data in data warehousing and data mining
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