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Data Warehouses and OLAP

Data Warehouses and OLAP
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Published Date:22-07-2017
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Data Warehouses and OLAP www.ThesisScientist.comData Warehousing and OLAP Technology for Data Mining  What is a data warehouse?  A multi-dimensional data model  Data warehouse architecture  Data warehouse implementation  Further development of data cube technology  From data warehousing to data mining www.ThesisScientist.comWhy Data Warehousing?  Data warehousing can be considered as an important preprocessing step for data mining Heterogeneous Databases data selection Data Warehouse data cleaning data integration data summarization  A data warehouse also provides on-line analytical processing (OLAP) tools for interactive multidimensional data analysis. www.ThesisScientist.comExample of a Data Warehouse (1) US-Database Data Warehouse Employee Department FACT table eid name birthdate did dname ... ... ... ... ... timeid pid sales 1 1 2 2 1 4 Transaction Details 2 2 1 tid type date tid pid qty 3 3 2 1 sale 4/11/1999 1 21 2 ... ... ... 2 sale 5/2/1999 2 13 1 3 buy 5/17/1999 3 41 3 dimension 1: time ... ... ... ... ... ... timeid day month year 1 11 4 1999 2 15 4 1999 HK-Database 3 2 5 1999 Supplier Country ... ... ... sid name birthdate cid cname ... ... ... ... ... dimension 2: product pid name type sid date time qty pid 1 chair office 1 15:4:1999 8:30 2 11 2 table office 2 15:4:1999 9:30 2 11 Sales 3 desk office 3 ??? 3 56 ... ... 4 19:5:1999 4 22 ... ...Example of a Data Warehouse (2)  Data Selection  Only data which are important for analysis are selected (e.g., information about employees, departments, etc. are not stored in the warehouse)  Therefore the data warehouse is subject-oriented  Data Integration  Consistency of attribute names  Consistency of attribute data types. (e.g., dates are converted to a consistent format)  Consistency of values (e.g., product-ids are converted to correspond to the same products from both sources)  Integration of data (e.g, data from both sources are integrated into the warehouse) www.ThesisScientist.comExample of a Data Warehouse (3)  Data Cleaning  Tuples which are incomplete or logically inconsistent are cleaned  Data Summarization  Values are summarized according to the desired level of analysis  For example, HK database records the daytime a sales transaction takes place, but the most detailed time unit we are interested for analysis is the day. www.ThesisScientist.comExample of a Data Warehouse (4)  Example of an OLAP query (collects counts)  Summarize all company sales according to product and year, and further aggregate on each of these dimensions. year 1999 2000 2001 2002 ALL chairs 25 37 89 21 172 tables 10 30 0 45 85 Data cube desks 56 84 9 35 184 shelves 19 20 0 71 110 boards 5 16 11 15 47 ALL 115 187 109 187 598 productWhat is Data Warehouse?  Defined in many different ways, but not rigorously.  A decision support database that is maintained separately from the organization‟s operational database  Support information processing by providing a solid platform of consolidated, historical data for analysis.  “A data warehouse is a subject-oriented, integrated, time-variant, and nonvolatile collection of data in support of management‟s decision-making process.”—W. H. Inmon  Data warehousing:  The process of constructing and using data warehouses www.ThesisScientist.comData Warehouse—Subject-Oriented  Organized around major subjects, such as customer, product, sales.  Focusing on the modeling 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. www.ThesisScientist.comData 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 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” (the time elements could be extracted from log files of transactions) www.ThesisScientist.comData Warehouse—Non-Volatile  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. www.ThesisScientist.comData 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 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 www.ThesisScientist.comData Warehouse vs. Heterogeneous DBMS  Example of a Heterogeneous DBMS Heterogeneous Databases mediator/ wrapper R1 Q1 results meta- R2 user data Q2 query R3 query Q3 transformation  The results from the various sources are integrated and returned to the user www.ThesisScientist.comData Warehouse vs. Heterogeneous DBMS  Advantages of a Data Warehouse:  The information is integrated in advance, therefore there is no overhead for (i) querying the sources and (ii) combining the results  There is no interference with the processing at local sources (a local source may go offline)  Some information is already summarized in the warehouse, so query effort is reduced.  When should mediators be used?  When queries apply on current data and the information is highly dynamic (changes are very frequent).  When the local sources are not collaborative. www.ThesisScientist.comData 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 www.ThesisScientist.comOLTP vs. OLAP OLTP OLAP users clerk, IT professional manager function day to day operations Decision support DB design application-oriented subject-oriented data current, up-to-date historical, detailed, flat relational summarized, multidimensional isolated integrated, consolidated usage repetitive ad-hoc access read/write lots of scans index/hash on prim. key unit of work short, simple transaction complex query records accessed tens millions users thousands hundreds DB size 100MB-GB 100GB-TB (even PB) metric transaction throughput query throughput, response 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, summarization) of data from heterogeneous sources  data quality: different sources typically use inconsistent data representations, codes and formats which have to be reconciled www.ThesisScientist.comData Warehousing and OLAP Technology for Data Mining  What is a data warehouse?  A multi-dimensional data model  Data warehouse architecture  Data warehouse implementation  Further development of data cube technology  From data warehousing to data mining www.ThesisScientist.comFrom Tables and Spreadsheets to Data Cubes  A data warehouse is based on a multidimensional data model which views data in the form of a data cube  A data cube, such as sales, allows data to be modeled and viewed in multiple dimensions  Dimension tables, such as item (item_name, brand, type), or time(day, week, month, quarter, year)  Fact table contains measures (such as dollars_sold) and keys to each of the related dimension tables