【正文】 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 OLAP system manages large amounts of historical data, provides 2 facilities for summarization and aggregation, and stores and manages information at different levels of 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 applicationoriented database 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 contrast, an OLAP system often spans multiple versions of a database schema, due to the evolutionary process of an systems also deal with information that originates from different organizations, integrating information from many data 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 a system requires concurrency control and recovery , accesses to OLAP systems are mostly readonly operations(since most data warehouses store historical rather than uptodate information), although many could be plex features which distinguish between OLTP and OLAP systems include database size, frequency of operations, and performance metrics and so , 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 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” the other hand, data warehouse queries are often involve the putation of large groups of data at summarized levels, and may require the use of special data organization, access, and implementation methods based on multidimensional OLAP queries in operational databases would substantially degrade the performance of operational , an operational database supports the concurrent processing of several control and recovery mechanisms, such as locking and logging, are required to ensure the consistency and robustness of OLAP query often needs readonly access of data records for summarization and 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 , the separation of operational databases from data warehouses is based on the different structures, contents, and uses of the data in these two support requires historical data, whereas operational databases do not typically maintain historical this context, the data in operational databases, though abundant, is usually far from plete for decision support requires consolidation(such as aggregation and summarization)of data from heterogeneous sources, resulting in high quality, cleansed and integrated contrast, operational databases contain only detailed raw data, such as transactions, which need to be consolidated before the two systems provide quite different functionalities and require different kinds of data, it is necessary to maintain separate 數(shù)據(jù)倉(cāng)庫(kù)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?！边@個(gè)簡(jiǎn)短，但是復(fù)合的定義表述了數(shù)據(jù)倉(cāng)庫(kù)的主要特點(diǎn)。(3)隨時(shí)間變化：數(shù)據(jù)被存儲(chǔ)是用來(lái)提供變化歷史角度的信息。數(shù)據(jù)倉(cāng)庫(kù)的構(gòu)造需要數(shù)據(jù)集成、數(shù)據(jù)清理和數(shù)據(jù)統(tǒng)一。許多組織收集了不同類的數(shù)據(jù)，并由多個(gè)異種的、自治的、分布的數(shù)據(jù)源維護(hù)大型數(shù)據(jù)庫(kù)。這種查詢驅(qū)動(dòng)的方法需要復(fù)雜的信息過(guò)濾和集成處理，并且與局部數(shù)據(jù)源上的處理競(jìng)爭(zhēng)資 5 源。此外，數(shù)據(jù)倉(cāng)庫(kù)存儲(chǔ)并集成歷史信息，支持復(fù)雜的查詢。這種系統(tǒng)稱為聯(lián)機(jī)分析處理（OLAP）系統(tǒng)。在線分析系統(tǒng)采用星形或雪花模型和面向主題的數(shù)據(jù)庫(kù)設(shè)計(jì)。其它區(qū)分聯(lián)機(jī)處理系統(tǒng)和在線分析系統(tǒng)包括數(shù)據(jù)大小，操作的頻率，性能的指標(biāo)。在線分析系統(tǒng)查詢通常需要對(duì)數(shù)據(jù)記錄進(jìn)行只讀訪問(wèn)，以進(jìn)行匯總和聚集。第二篇：畢業(yè)設(shè)計(jì)_英語(yǔ)論文翻譯┊ ┊ ┊ ┊ ┊ ┊ ┊ ┊ ┊ ┊ ┊ ┊ ┊ 裝 ┊ ┊ ┊ ┊ ┊ 訂 ┊ ┊ ┊ ┊ ┊ 線 ┊ ┊ ┊ ┊ ┊ ┊ ┊ ┊ ┊ ┊ ┊ ┊ ┊畢業(yè)設(shè)計(jì)（論文）報(bào)告紙LATHESThe basic machines that are designed primarily to do turning, facing and boring are called little turning is done on other types of machine tools, and nine can do it with equal lathe can do boring, facing, drilling, and reaming in addition to turning, their versatility permits several operations to be performed with a single setup of the accounts for the fact that lathes of various types are more widely used in manufacturing than any other machine in various forms have existed for more than two thousand lathes date from about 1797, when Henry Maudsley developed one with a provided controlled, mechanical feed of the ingenious Englishman also developed a changegear system that could connect the motions of the spindle and leadscrew and thus enable threads to be e