text: i have very simple solution, i have pdf file for you, just simply contact me and receive it,\n\nhere is some data inside pdf file.\n\nOn-Line Analytic Processing\nWe shall now take up an important class of applications for integrated informa-\ntion systems, especially data warehouses. Companies and organizations create\na warehouse with a copy of large amounts of their available data and assign\nanalysts to query this warehouse for patterns or trends of importance to the or-\nganization. This activity, called OLAP (standing for On-Line Analytic Process-\ning and pronounced &#92;oh-lap"), generally involves highly complex queries that\nuse one or more aggregations. These queries are often termed OLAP queries\nor decision-support queries. Some examples will be given in Section 20.4.1; a\ntypical example is to search for products with increasing or decreasing overall\nsales.\nDecision-support queries used in OLAP applications typically examine very\nlarge amounts of data, even if the query results are small. In contrast, common\ndatabase operations, such as bank deposits or airline reservations, each touch\nonly a tiny portion of the database; the latter type of operation is often referred\nto as OLTP (On-Line Transaction Processing, spoken &#92;oh-ell-tee-pee").\nRecently, new query-processing techniques have been developed that are\nespecially good at executing OLAP queries e�ectively. Furthermore, because of\nthe distinct nature of a certain class of OLAP queries, special forms of DBMS's\nhave been developed and marketed to support OLAP applications. The same\ntechnology is beginning to migrate to standard SQL systems, as well. We shall\ndiscuss the architecture of these systems in Section 20.5.\n20.4.1 OLAP Applications\nA commonOLAP application uses a warehouse of sales data. Major store chains\nwill accumulate terabytes of information representing every sale of every item\nat every store. Queries that aggregate sales into groups and identify signi�cant\ngroups can be of great use to the company in predicting future problems and\nopportunities.\nExample 20.27 : Suppose the Aardvark Automobile Co. builds a data ware-\nhouse to analyze sales of its cars. The schema for the warehouse might be:\nSales(serialNo, date, dealer, price)\nAutos(serialNo, model, color)\nDealers(name, city, state, phone)\nA typical decision-support query might examine sales on or after April 1, 2001\nto see how the recent average price per vehicle varies by state. Such a query is\nshown in Fig. 20.19.\nNotice how the query of Fig. 20.19 touches much of the data of the database,\nas it classi�es every recent Sales fact by the state of the dealer that sold it.\nIn contrast, common OLTP queries, such as &#92;�nd the price at which the auto\nwith serial number 123 was sold," would touch only a single tuple of the data.\n2\n20.4. ON-LINE ANALYTIC PROCESSING 1047\nWarehouses and OLAP\nThere are several reasons why data warehouses play an important role in\nOLAP applications. First, the warehouse may be necessary to organize\nand centralize corporate data in a way that supports OLAP queries; the\ndata may initially be scattered across many di�erent databases. But often\nmore important is the fact that OLAP queries, being complex and touch-\ning much of the data, take toomuch time to be executed in a transaction-\nprocessing system with high throughput requirements. OLAP queries of-\nten can be considered &#92;long transactions" in the sense of Section 19.7.\nLong transactions locking the entire database would shut down the\nordinary OLTP operations (e.g., recording new sales as they occur could\nnot be permitted if there were a concurrent OLAP query computing av-\nerage sales). A common solution is to make a copy of the raw data in a\nwarehouse, run OLAP queries only at the warehouse, and run the OLTP\nqueries and data modi�cations at the data sources. In a common sce-\nnario, the warehouse is only updated overnight, while the analysts 