Data Warehouse-lntegrated Constructed by integrating multiple, heterogeneous data Sources relational databases flat files on -line transaction records Data cleaning and data integration techniques are applied Ensure consistency in naming conventions, encoding structures attribute measures etc. among different data sources E.g., Hotel price: currency tax, breakfast covered, etc When data is moved to the warehouse it is converted
6 Data Warehouse—Integrated ◼ Constructed by integrating multiple, heterogeneous data sources ◼ relational databases, flat files, on-line transaction records ◼ Data cleaning and data integration techniques are applied. ◼ Ensure consistency in naming conventions, encoding structures, attribute measures, etc. among different data sources ◼ E.g., Hotel price: currency, tax, breakfast covered, etc. ◼ When data is moved to the warehouse, it is converted
Data warehouse-Time variant The time horizon ha'raiz(n for the data warehouse is significantly longer than that of operational systems Operational database: current value data Data warehouse data: provide information from a historical 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
7 Data Warehouse—Time Variant ◼ The time horizon [hə'raɪz(ə)n] for the data warehouse is significantly longer than that of operational systems ◼ Operational database: current value data ◼ Data warehouse data: provide information from a historical 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
Data Warehouse-Nonvolatile A physically separate store of data transformed from the operational environment Operational update of data does not occur in the data warehouse 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
8 Data Warehouse—Nonvolatile ◼ A physically separate store of data transformed from the operational environment ◼ Operational update of data does not occur in the data warehouse 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
Data wareh。 use Vs。 Heter。 geneous DBMS Traditional heterogeneous DB integration a query driven approach Build wrappers/mediators on top of heterogeneous databases When a query is posed to a client site a meta-dictionary is used to translate the query into queries appropriate for individual heterogeneous sites involved and the results are integrated into a global answer set Complex information filtering compete for resources Data warehouse: update-driven, high performance Information from heterogeneous sources is integrated in advance and stored in warehouses for direct query and analysis
9 Data Warehouse vs. Heterogeneous DBMS ◼ Traditional heterogeneous DB integration: A query driven approach ◼ Build wrappers/mediators on top of heterogeneous databases ◼ When a query is posed to a client site, a meta-dictionary is used to translate the query into queries appropriate for individual heterogeneous sites involved, and the results are integrated into a global answer set ◼ Complex information filtering, compete for resources ◼ Data warehouse: update-driven, high performance ◼ Information from heterogeneous sources is integrated in advance and stored in warehouses for direct query and analysis
Data 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
10 Data 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