Decision support systems for E-commerceDecision support systems for ECPotential Applications of Data Warehousing and Mining in ECData WarehousingCharacteristics (cont’d)Slide 8Reasons to Separate DW from Operational SystemsSlide 10System ArchitectureDW ComponentsDW Components (cont’d)Slide 14Multidimensional DataOLAP ServersWarehouse Design: Conceptual ModelingA Multidimensional fact table schemeExample of The Star SchemaExample of the Snowflake SchemaExample of the Fact Constellation SchemaSales DataA Sample Data CubeOLAP OperationsOLAP Operations (cont’d)Cube OperationCube Computation -- Array Based AlgorithmROLAP versus MOLAPSlide 29ExampleExample (Cont’d)Actual ApplicationPowerPoint PresentationSlide 35Slide 36Slide 37Data MiningData Mining (Cont’d)ClassificationDecision support systems for E-commerceDecision support systems for EC-DSS: help the knowledge worker (executive, manager, analyst) make faster and better decisions–what were the sales volumes by region and product category for the last year?–How did the share price of computer manufacturers correlate with quarterly profits over the past 10 years?–Will a 10% discount increase sales volume sufficiently?-Data Warehousing: enables On-line analytical processing (OLAP)–OLAP is a component of decision support system-Data mining–Extraction of interesting knowledge (rules, regularities, patterns, constraints) from data in large databases. –Data mining is a powerful, high-performance data analysis tool for decision support.Potential Applications of Data Warehousing and Mining in EC-Analysis of user access patterns and buying patterns-Customer segmentation and target marketing-Cross selling and improved Web advertisement-Personalization-Association (link) analysis-Customer classification and prediction-Time-series analysis -Typical event sequence and user behavior pattern analysis-Transition and trend analysisData Warehousing-The phrase data warehouse was coined by William Inmon in 1990-Data Warehouse is a decision support database that is maintained separately from the organization’s operational database-Definition: A DW is a repository of integrated information from distributed, autonomous, and possibly heterogeneous information sources for query, analysis, decision support, and data mining purposesCharacteristics (cont’d)-Integrated–No consistency in encoding, naming conventions, … among different application-oriented data from different legacy systems, different heterogeneous data sources–When data is moved to the warehouse, it is consolidated converted, and encoded-Non-volatile–New data is always appended to the database, rather than replaced–The database continually absorbs new data, integrating it with the previous data–In contrast, operational data is regularly accessed and manipulated a record at a time and update is done to data in the operational environment.Characteristics (cont’d)-Time-variant–The time horizon for the data warehouse is significantly longer than that of operational systems.–Operational database contain current value data. Data warehouse data is nothing more than a sophisticated series of snapshots, taken as of some moment in time. Operational data is valid only at the moment of access-capturing a moment in time. Within seconds, that data may no longer be valid in its description of current operations–Operational data may or may not contain some element of time. Informational data has a time dimension: each data point is associated with a point in time, and data points can be compared along that axis.Reasons to Separate DW from Operational Systems-Performance:–special data organization, access methods, and implementation methods are needed to support multidimensional views and operations typical of OLAP–Complex OLAP queries would degrade performance for operational transactions, Thus DW avoids interruption of the operational processing at the underlying information sources–Concurrency control and recovery of OLTP mode are not compatible with OLAP analysis –Provide fast access to integrated informationReasons to Separate DW from Operational Systems- Decision support requires–historical data which operational databases do not typically maintain–consolidation of data from heterogeneous sources: operational databases, external sources•different sources typically use inconsistent data representations, codes and formats which have to be reconciled.–aggregation, summarization, annotation of raw dataSystem Architecture DetectorDetectorDetectorDetectorEnd UserEnd UserLegacyLegacyFlat-fileFlat-fileRDBMSRDBMSOODBMSOODBMS. . .. . .Analysis, Query Reports,Analysis, Query Reports,Data MiningData MiningDW Components-Underlying information sources–often the operational systems, providing the lowest level of data.–designed for operational use, not for decision support, and the data reflect this fact.–Multiple data sources are often from different systems run on a wide range of hardware and much of the software is built in-house or highly customized. –Multiple data sources introduce a large number of issues, such as semantic conflicts.–Distributed, autonomous, and possibly heterogeneousDW Components (cont’d)-Integrator–Receives updates–makes the data conform to the conceptual schema used by the warehouse–integrates the changes into the warehouse–merges the data with existing data already present–resolves possible update anomalies–Modifies warehouse views accordingly-User interface–Tools to query and perform data analysis and data miningDW Components (cont’d)-Change detectors/propagators–Refresh the warehouse by detecting to an information source that are of interest to the warehouse and propagating updates on source data to the data stored in the warehouse–when to refresh•determined by usage, types of data source, etc.–how to refresh•data shipping: using triggers to update snapshot log table and propagate the updated data to the warehouse (define triggers in a full-functionality DBMS)•transaction shipping: shipping the updates in the transaction log (examine the updates in the log file)•write programs for legacy systemsMultidimensional Data-Sales volume as a function of product, time, and geographyOLAP Servers-Relational OLAP (ROLAP)–Extended relational DBMS that maps operations on multidimensional data to standard relations operations-Multidimensional OLAP (MOLAP)–Special purpose server that directly
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