【正文】 ent anizations, integrating information from many data stores. Because of their huge volume, OLAP data are stored on multiple storage media. (5). Access patterns: The access patterns of an OLTP system consist mainly of short, atomic transactions. Such a system requires concurrency control and recovery mechanisms. However, accesses to OLAP systems are mostly readonly operations (since most data warehouses store historical rather than uptodate information), although many could be plex queries. Other features which distinguish between OLTP and OLAP systems include database size, frequency of operations, and performance metrics and so on. , why have a separate data warehouse? “Since operational databases store huge amounts of data, you observe, “why not perform online analytical processing directly on such databases instead of spending additional time and resources to construct a separate data warehouse? A major reason for such a separation is to help promote the high performance of both systems. An operational database is designed and tuned from known tasks and workloads, such as indexing and hashing using primary keys, searching for particular records, and optimizing “canned queries. On the other hand, data warehouse queries are often plex. They involve the putation of large groups of data at summarized levels, and may require the use of special data anization, access, and implementation methods based on multidimensional views. Processing OLAP queries in operational databases would substantially degrade the performance of operational tasks. Moreover, an operational database supports the concurrent processing of several transactions. Concurrency control and recovery mechanisms, such as locking and logging, are required to ensure the consistency and robustness of transactions. An OLAP query often needs readonly access of data records for summarization and aggregation. Concurrency control and recovery mechanisms, if applied for such OLAP operations, may jeopardize the execution of concurrent transactions and thus substantially reduce the throughput of an OLTP system. Finally, the separation of operational databases from data warehouses is based on the different structures, contents, and uses of the data in these two systems. Decision support requires historical data, whereas operational databases do not typically maintain historical data. In this context, the data in operational databases, though abundant, is usually far from plete for decision making. Decision support requires consolidation (such as aggregation and summarization) of data from heterogeneous sources, resulting in high quality, cleansed and integrated data. In contrast, operational databases contain only detailed raw data, such as transactions, which need to be consolidated before analysis. Since the two systems provide quite different functionalities and require different kinds of data, it is necessary to maintain separate databases. 4 數(shù)據(jù)倉庫 數(shù)據(jù)倉庫為商務(wù)運(yùn)作提供了組織結(jié)構(gòu)和工具，以便系統(tǒng)地組織、理解和使用數(shù)據(jù)進(jìn)行決策。許多組織發(fā)現(xiàn)在如今的具有競爭與快速發(fā)展的世界中數(shù)據(jù)倉庫是非常有用的工具。 在最近的幾年里，許多公司花了幾百萬美元用于構(gòu)建企業(yè)數(shù)據(jù)庫。許多人也認(rèn)為隨著競爭加劇，數(shù)據(jù)倉庫己成為營銷必備的手段 —— 一種了解顧客的需求的武器。 “那么”，你可能會充滿神秘地問，“到底什么是數(shù)據(jù)倉庫？” 數(shù)據(jù)倉庫有不同的定義，但卻很難有一個嚴(yán)格的定義。不嚴(yán)謹(jǐn)?shù)恼f，數(shù)據(jù)倉庫是一個數(shù)據(jù)庫，它與組織機(jī)構(gòu)的操作數(shù)據(jù)庫分別維護(hù)。數(shù)