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Creating a "One-row-per-subject" data mart is a fundamental task when preparing data for data mining. To answer the underlying business question, the analyst or data-mart programmer is challenged to distill the relevant information from various data sources. Creating a data mart involves more than reading columns from a source table to the data mart. It also includes the aggregation or transposition of observations from "Multiple-row-per-subject" tables like transactional tables and time histories. This paper shows the details of input data structures for a "One-row-per-subject" data mart. It also discusses the "One-row-per-subject" paradigm from a technical and a business point of view, and shows how data, some of which include hierarchical dependencies, is aggregated into a "Single-row-per-subject.".

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Format:PDFSize:236 KB
Date:Feb 2006
Pages:16

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