【正文】 ultidimensional queries. As a result, data warehousing has bee very popular in industry. 1. Differences between operational database systems and data warehouses Since 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 systems. The major task of online operational database systems is to perform online transaction and query processing. These systems are called online transaction processing (OLTP) systems. They cover most of the daytoday operations of an anization, such as, purchasing, inventory, manufacturing, banking, payroll, registration, and accounting. Data warehouse systems, on the other hand, serve users or “knowledge workers in the role of data analysis and decision making. Such systems can anize and present data in various formats in order to acmodate the diverse needs of the different users. These systems are known as online analytical processing (OLAP) systems. The 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 professionals. An 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 making. An OLAP system manages large amounts of historical data, provides facilities for summarization and aggregation, and stores and manages information at different levels of granularity. These features make the data easier for use in informed decision making. (3). Database design: An OLTP system usually adopts an entityrelationship (ER) data model and an application oriented database design. An OLAP system typically adopts either a star or snowflake model, and a subjectoriented database design. (4). View: An OLTP system focuses mainly on the current data within an enterprise or department, without referring to historical data or data in different anizations. In contrast, an OLAP system often spans multiple versions of a database schema, due to the evolutionary process of an anization. OLAP systems also deal with information that originates from different 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. 2. But, 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. 數(shù)據(jù)倉庫 數(shù)據(jù)倉庫為商務(wù)運(yùn)作提供結(jié)構(gòu)與工具，以便系統(tǒng)地組織、理解和使用數(shù)據(jù)進(jìn)行決策。s operational databases. Data warehouse systems allow for the integration of a variety of application systems. They support information processing by providing a solid platform of consolidated, historical data for analysis. According to W. H. Inmon, a leading architect in the construction of data warehouse systems, “a data warehouse is a subjectoriented, integrated, timevariant, and nonvolatile collection of data in support of management39。