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dc.contributor.authorReddy, V M-
dc.contributor.authorJena, S K-
dc.identifier.citationInternational Conference on Information and Knowledge Engineering, 2010 (IKE'10), P 196-201en
dc.descriptionCopyright belongs to the Proceedings Publisher WORLDCOMP’10.en
dc.description.abstractOver the years, DataWarehousing has gone through a number of evolutions from a relatively simple reporting database to sophisticated analytical applications such as analyzing customer lifetime values, market basket analyses, potentially defecting customers, fraud patterns, inventory churns, and so on. In all, though, these static sets of data could not give us the most current and recent changes of data necessary to act upon the results of Business Intelligence analyses. So, the traditional DataWarehouses are Static in nature. They are not showing any dynamics in their structure and content. In this paper we present a methodology on how to adapt data warehouse schemas and loading the fresh data with the help of the Tool based [12][13] Extraction, Transformation and Loading (ETL) procedure for Active Data Warehouse. To achieve the dynamic nature for the DataWarehouse, we are using Ricardo-Jorge [1] techniques such as table structure replication and query predicate restrictions for selecting data, to enable continuously loading data in the DataWarehouse with minimum impact in query execution time.en
dc.format.extent565877 bytes-
dc.subjectActive DataWarehouseen
dc.subjectReal-Time DataWarehouseen
dc.subjectData Acquisitionen
dc.subjectcode based ETLen
dc.subjectTool based ETLen
dc.titleActive Datawarehouse Loading by Tool Based ETL Procedureen
Appears in Collections:Conference Papers

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