Topics What is a data warehouse? A multi-dimensional data model Data warehouse architecture Data warehouse implementation From data warehousing to data mining2What is Data Warehouse? Defined in many different ways, but not rigorously. A decision support database that is maintained separately fromthe organization’s operational database Support information processing by providing a solid platform ofconsolidated, historical data for analysis. “A data warehouse is a subject-oriented, integrated, time-variant,and nonvolatile collection of data in support of management’sdecision-making process.”—W. H. Inmon Data warehousing: The process of constructing and using data warehouses3Data Warehouse—Subject-Oriented Organized around major subjects, such as customer,product, sales. Focusing on the modeling and analysis of data for decisionmakers, not on daily operations or transaction processing. Provide a simple and concise view around particularsubject issues by excluding data that are not useful in thedecision support process.4Data Warehouse—Integrated Constructed by integrating multiple, heterogeneous datasources relational databases, flat files, on-line transactionrecords Data cleaning and data integration techniques areapplied. Ensure consistency in naming conventions, encodingstructures, attribute measures, etc. among differentdata sources E.g., Hotel price: currency, tax, breakfast covered, etc. When data is moved to the warehouse, it isconverted.5Data Warehouse—Time Variant The time horizon for the data warehouse is significantlylonger than that of operational systems. Operational database: current value data. Data warehouse data: provide information from ahistorical perspective (e.g., past 5-10 years) Every key structure in the data warehouse Contains an element of time, explicitly or implicitly But the key of operational data may or may not contain“time element”.6Data Warehouse—Non-Volatile A physically separate store of data transformed from theoperational environment. Operational update of data does not occur in the datawarehouse environment. Does not require transaction processing, recovery,and concurrency control mechanisms Requires only two operations in data accessing: initial loading of data and access of data.7Data Warehouse vs. Heterogeneous DBMS Traditional heterogeneous DB integration: Build wrappers/mediators on top of heterogeneous databases Query driven approach When a query is posed to a client site, a meta-dictionary isused to translate the query into queries appropriate forindividual heterogeneous sites involved, and the results areintegrated into a global answer set Complex information filtering, compete for resources Data warehouse: update-driven, high performance Information from heterogeneous sources is integrated in advanceand stored in warehouses for direct query and analysis8Data Warehouse vs. Operational DBMS OLTP (on-line transaction processing) Major task of traditional relational DBMS Day-to-day operations: purchasing, inventory, banking,manufacturing, payroll, registration, accounting, etc. OLAP (on-line analytical processing) Major task of data warehouse system Data analysis and decision making Distinct features (OLTP vs. OLAP): User and system orientation: customer vs. market Data contents: current, detailed vs. historical, consolidated Database design: ER + application vs. star + subject View: current, local vs. evolutionary, integrated Access patterns: update vs. read-only but complex queries
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