354.DataWarehousing

354.DataWarehousing - Database Systems I Data Warehousing...

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CMPT 354, Simon Fraser University, Fall 2008, Martin Ester 1 Database Systems I Data Warehousing
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CMPT 354, Simon Fraser University, Fall 2008, Martin Ester 2 Introduction Increasingly, organizations are analyzing current and historical data to identify useful patterns and support business strategies ( Decision Support ). Emphasis is on complex, interactive, exploratory analysis of very large datasets created by integrating data from across all parts of an enterprise; data is fairly static. Contrast such On-Line Analytic Processing (OLAP) with traditional On-line Transaction Processing (OLTP) : mostly
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CMPT 354, Simon Fraser University, Fall 2008, Martin Ester 3 DBS for Decision Support Data Warehouse: Consolidate data from many sources in one large repository. Loading, periodic synchronization of replicas. Semantic integration. OLAP: Complex SQL queries and views. Queries based on “multidimensional” view of data and spreadsheet-style operations. Interactive and “online” ( manual ) analysis. Data Mining : Automatic discovery of interesting trends and other patterns.
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CMPT 354, Simon Fraser University, Fall 2008, Martin Ester 4 Data Warehousing A Data Warehouse is a subject oriented, integrated, time variant, non volatile collection of data for the purpose of decision support. Integrates data from several operational (OLTP) databases. Keeps (relevant part of the) history of the data. Views data at a more abstract level than OLTP systems (aggregate over many detail records).
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CMPT 354, Simon Fraser University, Fall 2008, Martin Ester 5 Data Warehouse Architecture EXTERNAL DATA SOURCES EXTRACT INTEGRATE TRANSFORM LOAD / REFRESH DATA WAREHOUSE Metadata Repository SUPPORTS OLAP DATA MINING
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CMPT 354, Simon Fraser University, Fall 2008, Martin Ester 6 Data Warehousing Integrated data spanning long time periods, often augmented with summary information. Data warehouse keeps the history. Therefore, several gigabytes to terabytes common. Interactive response times expected for complex queries. On the other hand, ad-hoc updates uncommon.
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CMPT 354, Simon Fraser University, Fall 2008, Martin Ester 7 Data Warehousing Issues Semantic integration: When getting data from multiple sources, must eliminate mismatches, e.g., different currencies, DB schemas. Heterogeneous sources: Must access data from a variety of source formats and repositories. Replication capabilities can be exploited here. Load, refresh, purge: Must load data, periodically refresh it, and purge too-old data. Metadata management: Must keep track of source, loading time, and other information for all data in the warehouse.
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CMPT 354, Simon Fraser University, Fall 2008, Martin Ester 8 Multidimensional Data Model Consists of a collection of dimensions (independent variables) and (numeric) measures (dependent variables). Each entry (cell) aggregates the value(s) of the measure(s) for all records that fall into that cell, i.e. for all records that in each dimension have attribute values corresponding to the value of the cell in this dimension.
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This note was uploaded on 10/07/2009 for the course CS 12601 taught by Professor Kewang during the Spring '09 term at Simon Fraser.

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354.DataWarehousing - Database Systems I Data Warehousing...

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