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A:i have very simple solution, i have pdf file for you, just simply contact me and receive it, here is some data inside pdf file. On-Line Analytic Processing We shall now take up an important class of applications for integrated informa- tion systems, especially data warehouses. Companies and organizations create a warehouse with a copy of large amounts of their available data and assign analysts to query this warehouse for patterns or trends of importance to the or- ganization. This activity, called OLAP (standing for On-Line Analytic Process- ing and pronounced \oh-lap"), generally involves highly complex queries that use one or more aggregations. These queries are often termed OLAP queries or decision-support queries. Some examples will be given in Section 20.4.1; a typical example is to search for products with increasing or decreasing overall sales. Decision-support queries used in OLAP applications typically examine very large amounts of data, even if the query results are small. In contrast, common database operations, such as bank deposits or airline reservations, each touch only a tiny portion of the database; the latter type of operation is often referred to as OLTP (On-Line Transaction Processing, spoken \oh-ell-tee-pee"). Recently, new query-processing techniques have been developed that are especially good at executing OLAP queries eectively. Furthermore, because of the distinct nature of a certain class of OLAP queries, special forms of DBMS's have been developed and marketed to support OLAP applications. The same technology is beginning to migrate to standard SQL systems, as well. We shall discuss the architecture of these systems in Section 20.5. 20.4.1 OLAP Applications A commonOLAP application uses a warehouse of sales data. Major store chains will accumulate terabytes of information representing every sale of every item at every store. Queries that aggregate sales into groups and identify signicant groups can be of great use to the company in predicting future problems and opportunities. Example 20.27 : Suppose the Aardvark Automobile Co. builds a data ware- house to analyze sales of its cars. The schema for the warehouse might be: Sales(serialNo, date, dealer, price) Autos(serialNo, model, color) Dealers(name, city, state, phone) A typical decision-support query might examine sales on or after April 1, 2001 to see how the recent average price per vehicle varies by state. Such a query is shown in Fig. 20.19. Notice how the query of Fig. 20.19 touches much of the data of the database, as it classies every recent Sales fact by the state of the dealer that sold it. In contrast, common OLTP queries, such as \nd the price at which the auto with serial number 123 was sold," would touch only a single tuple of the data. 2 20.4. ON-LINE ANALYTIC PROCESSING 1047 Warehouses and OLAP There are several reasons why data warehouses play an important role in OLAP applications. First, the warehouse may be necessary to organize and centralize corporate data in a way that supports OLAP queries; the data may initially be scattered across many dierent databases. But often more important is the fact that OLAP queries, being complex and touch- ing much of the data, take toomuch time to be executed in a transaction- processing system with high throughput requirements. OLAP queries of- ten can be considered \long transactions" in the sense of Section 19.7. Long transactions locking the entire database would shut down the ordinary OLTP operations (e.g., recording new sales as they occur could not be permitted if there were a concurrent OLAP query computing av- erage sales). A common solution is to make a copy of the raw data in a warehouse, run OLAP queries only at the warehouse, and run the OLTP queries and data modications at the data sources. In a common sce- nario, the warehouse is only updated overnight, while the analysts work on a frozen copy during the day. The warehouse data thus gets out of date by as much as 24 hours, which limits the timeliness of its answers to OLAP queries, but the delay is tolerable in many decision.....

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