Data mining 2 - Data warehouse (cheat sheet - printable)
- 1. Data Mining Lecture 2 What is Data Warehouse? Defined in many different ways, but not rigorously A decision support database that is maintained separately from the organisation’s operational database Support information processing by providing a solid platform of consolidated, historical data for analysis Definition by Inmon “A data warehouse is a subject-oriented, integrated, time-variant, and non-volatile collection of data in support of management’s decision-making process” Data warehousing The process of constructing and using data warehouses Data Warehouse—Subject-Oriented Organised around major subjects, such as customer, product, sales Focusing on the modelling and analysis of data for decision makers, not on daily operations or transaction processing Provide a simple and concise view around particular subject issues by excluding data that are not useful in the decision support process 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 o 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 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—Non-Volatile - Physically separate stores 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 Warehouse vs. Heterogeneous DB Traditional heterogeneous DB integration - Build wrappers/mediators on top of heterogeneous databases - Query driven approach o 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 o 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 access and analysis
- 2. Data Warehouse vs. Operational DB OLTP (On-Line Transaction Processing) Major task of traditional relational DB 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 OLTP vs. OLAP Why Separate Data Warehouse? High performance for both systems DBMS— tuned for OLTP -access methods, indexing, concurrency control, recovery Warehouse—tuned for OLAP -complex OLAP queries, multidimensional view, consolidation Different functions and different data Missing data: Decision support requires historical data which operational DBs do not typically maintain Data consolidation: DS requires consolidation (aggregation, summarisation) of data from heterogeneous sources Data quality: different sources typically use inconsistent data representations, codes and formats which have to be reconciled