【正文】 ating multiple heterogeneous sources, such as relational databases, flat files, and online transaction cleaning and data integration techniques are applied to ensure consistency in naming conventions, encoding structures, attribute measures, and so on..(3)Timevariant: Data are stored to provide information from a historical perspective(., the past 510 years).Every key structure in the data warehouse contains, either implicitly or explicitly, an element of time.(4)Nonvolatile: A data warehouse is always a physically separate store of data transformed from the application data found in the operational to this separation, a data warehouse does not require transaction processing, recovery, and concurrency control usually requires only two operations in data accessing: initial loading of data and access of data..In sum, a data warehouse is a semantically consistent data store that serves as a physical implementation of a decision support data model and stores the information on which an enterprise needs to make strategic data warehouse is also often viewed as an architecture, constructed by integrating data from multiple heterogeneous sources to support structured and/or ad hoc queries, analytical reporting, and decision making.“OK”, you now ask, “what, then, is data warehousing?“Based on the above, we view data warehousing as the process of constructing and using data construction of a data warehouse requires data integration, data cleaning, and data utilization of a data warehouse often necessitates a collection of decision support allows “knowledge workers”(., managers, analysts, and executives)to use the warehouse to quickly and conveniently obtain an overview of the data, and to make sound decisions based on information in the authors use the term “data warehousing“ to refer only to 1 the process of data warehouse construction, while the term warehouse DBMS is used to refer to the management and utilization of data will not make this distinction here.“How are organizations using the information from data warehouses?” Many organizations are using this information to support business decision making activities, including:(1)increasing customer focus, which includes the analysis of customer buying patterns(such as buying preference, buying time, budget cycles, and appetites for spending).(2)repositioning products and managing product portfolios by paring the performance of sales by quarter, by year, and by geographic regions, in order to finetune production strategies.(3)analyzing operations and looking for sources of profit.(4)managing the customer relationships, making environmental corrections, and managing the cost of corporate warehousing is also very useful from the point of view of heterogeneous database organizations typically collect diverse kinds of data and maintain large databases from multiple, heterogeneous, autonomous, and distributed information integrate such data, and provide easy and efficient access to it is highly desirable, yet effort has been spent in the database industry and research munity towards achieving this traditional database approach to heterogeneous database integration is to build wrappers and integrators(or mediators)on top of multiple, heterogeneous variety of data joiner and data blade products belong to this a query is posed to a client site, a metadata dictionary is used to translate the query into queries appropriate for the individual heterogeneous sites queries are then mapped and sent to local query results returned from the different sites are integrated into a global answer querydriven approach requires plex information filtering and integration processes, and petes for resources with processing at local is inefficient and potentially expensive for frequent queries, especially for queries requiring warehousing provides an interesting alternative to the traditional approach of heterogeneous database integration described than using a querydriven approach, data warehousing employs an updatedriven approach in which information from multiple, heterogeneous sources is integrated in advance and stored in a warehouse for direct querying and online transaction processing databases, data warehouses do not contain the most current , a data warehouse brings high performance to the integrated heterogeneous database system since data are copied, preprocessed, integrated, annotated, summarized, and restructured into one semantic data , query processing in data warehouses does not interfere with the processing at local , data warehouses can store and integrate historical information and support plex multidimensional a result, data warehousing has bee very popular in between operational database systems and data warehousesSince most people are familiar with mercial relational database systems, it is easy to understand what a data warehouse is by paring these two kinds of major task of online operational database systems is to perform online transaction and query systems are called online transaction processing(OLTP) cover most of the daytoday operations of an organization, such as, purchasing, inventory, manufacturing, banking, payroll, registration, and warehouse systems, on the other hand, serve users or “knowledge workers“ in the role of data analysis and decision systems can organize and present data in various formats in order to acmodate the diverse needs of the different systems are known as online analytical processing(OLAP) major distinguishing features between OLTP and OLAP are summarized as follows.(1)Users and system orientation: An OLTP system is customeroriented and is used for transaction and query processing by clerks, clients, and information technology OLAP system is marketoriented and is used for data analysis by knowledge workers, including managers, executives, and analysts.(2)Data contents: An OLTP system manages current data that, typically, are too detailed to be easily used for decision