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2025-01-27 02:15本頁(yè)面
【正文】 lementation of a decision support data model and stores the information on which an enterprise needs to make strategic decisions. A 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 warehouses. The construction of a data warehouse requires data integration, data cleaning, and data consolidation. The utilization of a data warehouse often necessitates a collection of decision support technologies. This 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 warehouse. Some authors use the term “data warehousing to refer only to 2 the process of data warehouse construction, while the term warehouse DBMS is used to refer to the management and utilization of data warehouses. We will not make this distinction here. “How are anizations using the information from data warehouses? Many anizations 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 assets. Data warehousing is also very useful from the point of view of heterogeneous database integration. Many anizations typically collect diverse kinds of data and maintain large databases from multiple, heterogeneous, autonomous, and distributed information sources. To integrate such data, and provide easy and efficient access to it is highly desirable, yet challenging. Much effort has been spent in the database industry and research munity towards achieving this goal. The traditional database approach to heterogeneous database integration is to build wrappers and integrators (or mediators) on top of multiple, heterogeneous databases. A variety of data joiner and data blade products belong to this category. When 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 involved. These queries are then mapped and sent to local query processors. The results returned from the different sites are integrated into a global answer set. This querydriven approach requires plex information filtering and integration processes, and petes for resources with processing at local sources. It is inefficient and potentially expensive for frequent queries, especially for queries requiring aggregations. Data warehousing provides an interesting alternative
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