Mt Holyoke CS 341 - Data Warehousing and Decision Support

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Data Warehousing and Decision SupportIntroductionThree Complementary TrendsData WarehousingWarehousing IssuesMultidimensional Data ModelMOLAP vs ROLAPDimension HierarchiesOLAP QueriesSlide 10Comparison with SQL QueriesThe CUBE OperatorDesign IssuesImplementation IssuesJoin IndexesBitmapped Join IndexQuerying Sequences in SQL:1999The WINDOW ClauseTop N QueriesSlide 20Online AggregationSummaryDatabase Management Systems, 2nd Edition. R. Ramakrishnan and J. Gehrke 1Data Warehousing and Decision SupportChapter 25, Part ADatabase Management Systems, 2nd Edition. R. Ramakrishnan and J. Gehrke 2Introduction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, 2nd Edition. R. Ramakrishnan and J. Gehrke 3Three 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!)Database Management Systems, 2nd Edition. R. Ramakrishnan and J. Gehrke 4Data 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 EXTRACTTRANSFORM LOAD REFRESH DATAWAREHOUSE MetadataRepositorySUPPORTSOLAPDATAMININGDatabase Management Systems, 2nd Edition. R. Ramakrishnan and J. Gehrke 5Warehousing 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, 2nd Edition. R. Ramakrishnan and J. Gehrke 6Multidimensional 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 1030 20 5025 8 15 1 2 3 timeid pid11 12 13pidtimeidlocidsaleslocidSlice locid=1is shown:Database Management Systems, 2nd Edition. R. Ramakrishnan and J. Gehrke 7MOLAP 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 systems.The main relation, which relates dimensions to a measure, is called the fact table. Each dimension can have additional attributes and an associated dimension table.E.g., Products(pid, pname, category, price)Fact tables are much larger than dimensional tables.Database Management Systems, 2nd Edition. R. Ramakrishnan and J. Gehrke 8Dimension HierarchiesFor each dimension, the set of values can be organized in a hierarchy:PRODUCT TIME LOCATIONcategory week month statepname date city yearquarter countryDatabase Management Systems, 2nd Edition. R. Ramakrishnan and J. Gehrke 9OLAP QueriesInfluenced by SQL and by spreadsheets.A common operation is to aggregate a measure over one or more dimensions.Find total sales.Find total sales for each city, or for each state.Find top five products ranked by total sales.Roll-up: Aggregating at different levels of a dimension hierarchy. E.g., Given total sales by city, we can roll-up to get sales by state.Database Management Systems, 2nd Edition. R. Ramakrishnan and J. Gehrke 10OLAP QueriesDrill-down: The inverse of roll-up. E.g., Given total sales by state, can drill-down to get total sales by city.E.g., Can also drill-down on different dimension to get total sales by product for each state.Pivoting: Aggregation on selected dimensions.E.g., Pivoting on Location and Time yields this cross-tabulation:63 81 14438 107 14575 35 110 WI CA Total199519961997176 223 339Total Slicing and Dicing: Equality and range selections on one or more dimensions.Database Management Systems, 2nd Edition. R. Ramakrishnan and J. Gehrke 11Comparison with SQL QueriesThe cross-tabulation obtained by pivoting can also be computed using a collection of SQLqueries:SELECT SUM(S.sales)FROM Sales S, Times T, Locations LWHERE S.timeid=T.timeid AND S.timeid=L.timeidGROUP BY T.year, L.stateSELECT SUM(S.sales)FROM Sales S, Times TWHERE S.timeid=T.timeidGROUP BY T.yearSELECT SUM(S.sales)FROM Sales S, Location LWHERE S.timeid=L.timeidGROUP BY L.stateDatabase Management Systems, 2nd Edition. R. Ramakrishnan and J. Gehrke 12The CUBE OperatorGeneralizing the previous example, if there are k dimensions, we have 2^k possible SQL GROUP BY queries that can be generated through pivoting on a subset of dimensions.CUBE pid, locid, timeid BY SUM SalesEquivalent to rolling up Sales on all eight subsets of the set {pid, locid, timeid}; each roll-up corresponds to an SQL query of the form:SELECT SUM(S.sales)FROM Sales SGROUP BY grouping-listLots of work on optimizing the CUBE operator!Database Management Systems, 2nd Edition. R. Ramakrishnan and J. Gehrke 13Design IssuesFact table in BCNF; dimension tables un-normalized.Dimension tables are small; updates/inserts/deletes are rare. So, anomalies less important than query performance.This kind of schema is very common in OLAP applications, and is called a star schema; computing the join of all these relations is called a star join. pricecategorypnamepid countrystatecitylocidsaleslocidtimeidpidholiday_flagweekdatetimeid


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