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 2IntroductionIncreasingly, 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 TrendsData 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 WarehousingIntegrated 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 IssuesSemantic 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 ModelCollection 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 ROLAPMultidimensional 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 HierarchiesFor 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 QueriesInfluenced 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 QueriesDrill-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 QueriesThe 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 OperatorGeneralizing 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 SalesEquivalent 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 IssuesFact 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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