Ch23a_DecSup-95 - 1 Database Management Systems, 2 nd...

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Unformatted text preview: 1 Database Management Systems, 2 nd Edition. R. Ramakrishnan and J. Gehrke 1 Data Warehousing and Decision Support Chapter 23, Part A Database Management Systems, 2 nd Edition. R. Ramakrishnan and J. Gehrke 2 Introduction ¡ Increasingly, organizations are analyzing current and historical data to identify useful patterns and support business strategies. ¡ 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 long queries, instead of short update Xacts. Database Management Systems, 2 nd Edition. R. Ramakrishnan and J. Gehrke 3 Three Complementary Trends ¡ Data Warehousing: 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 spreadsheet-style operations and “multidimensional” view of data. ¢ Interactive and “online” queries. ¡ Data Mining: Exploratory search for interesting trends and anomalies. (Another lecture!) 2 Database Management Systems, 2 nd Edition. R. Ramakrishnan and J. Gehrke 4 Data Warehousing ¡ Integrated data spanning long time periods, often augmented with summary information. ¡ Several gigabytes to terabytes common. ¡ Interactive response times expected for complex queries; ad-hoc updates uncommon. EXTERNAL DATA SOURCES EXTRACT TRANSFORM LOAD REFRESH DATA WAREHOUSE Metadata Repository SUPPORTS OLAP DATA MINING Database Management Systems, 2 nd Edition. R. Ramakrishnan and J. Gehrke 5 Warehousing Issues ¡ Semantic Integration: When getting data from multiple sources, must eliminate mismatches, e.g., different currencies, 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. Database Management Systems, 2 nd Edition. R. Ramakrishnan and J. Gehrke 6 Multidimensional Data Model ¡ Collection of numeric measures, which depend on a set of dimensions. ¢ E.g., measure Sales , dimensions Product (key: pid), Location (locid), and Time (timeid). 8 10 10 30 20 50 25 8 15 1 2 3 timeid p i d 1 1 1 2 1 3 11 1 1 25 11 2 1 8 11 3 1 15 12 1 1 30 12 2 1 20 12 3 1 50 13 1 1 8 13 2 1 10 13 3 1 10 11 1 2 35 p i d t i m e i d l o c i d s a l e s locid Slice locid=1 is shown: 3 Database Management Systems, 2 nd Edition. R. Ramakrishnan and J. Gehrke 7 MOLAP vs ROLAP ¡ Multidimensional data can be stored physically in a (disk-resident, persistent) array; called MOLAP systems. Alternatively, can store as a relation; called ROLAP...
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This note was uploaded on 02/06/2010 for the course CSE 302 taught by Professor Joel during the Summer '05 term at Punjab Engineering College.

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Ch23a_DecSup-95 - 1 Database Management Systems, 2 nd...

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