大学毕业设计仓库管理系统数据库计算机外文参考文献原文及翻译

河北工程大学毕业论文（设计）英文参考文献原文复印件及译文

数据仓库

数据仓库为商务运作提供结构与工具，以便系统地组织、理解和使用数据进行决策。大量组织机构已经发现，在当今这个充满竞争、快速发展的世界，数据仓库是一个有价值的工具。在过去的几年中，许多公司已花费数百万美元，建立企业范围的数据仓库。许多人感到，随着工业竞争的加剧，数据仓库成了必备的最新营销武器——通过更多地了解客户需求而保住客户的途

径。“那么”，你可能会充满神秘地问，“到底什么是数据仓库？”

数据仓库已被多种方式定义，使得很难严格地定义它。宽松地讲，数据仓库是一个数据库，它与组织机构的操作数据库分别维护。数据仓库系统允许将各种应用系统集成在一起，为统一的历史数据分析提供坚实的平台，对信息处理提供支持。

按照W. H. Inmon，一位数据仓库系统构造方面的领头建筑师的说法，“数

(1)

视图。

(2)

般文件和联机事务处理记录，集成在一起。使用数据清理和数据集成技术，确保命名约定、编码结构、属性度量的一致性等。

(3)时变的：数据存储从历史的角度（例如，过去5-10 年）提供信息。数据仓库中的关键结构，隐式或显式地包含时间元素。

(4) 非易失的：数据仓库总是物理地分离存放数据；这些数据源于操作环境下的应用数据。由于这种分离，数据仓库不需要事务处理、恢复和并行控制机制。通常，它只需要两种数据访问：数据的初始化装入和数据访问。

概言之，数据仓库是一种语义上一致的数据存储，它充当决策支持数据模型的物理实现，并存放企业决策所需信息。数据仓库也常常被看作一种体系结构，通过将异种数据源中的数据集成在一起而构造，支持结构化和启发式查询、分析报告和决策制定。

“好”，你现在问，“那么，什么是建立数据仓库？”

根据上面的讨论，我们把建立数据仓库看作构造和使用数据仓库的过程。数据仓库的构造需要数据集成、数据清理、和数据统一。利用数据仓库常常需要一些决策支持技术。这使得“知识工人”（例如，经理、分析人员和主管）能够使用数据仓库，快捷、方便地得到数据的总体视图，根据数据仓库中的信息做出准确的决策。有些作者使用术语“建立数据仓库”表示构造数据仓库的过程，而用术语“仓库DBMS”表示管理和使用数据仓库。我们将不区分二者。

“组织机构如何使用数据仓库中的信息？”许多组织机构正在使用这些信息支持商务决策活动，包括:

(1)、增加顾客关注，包括分析顾客购买模式（如，喜爱买什么、购买时间、预算周期、消费习惯）；

(2)、根据季度、年、地区的营销情况比较，重新配置产品和管理投资，调整生产策略；

(3)、分析运作和查找利润源；

(4)、管理顾客关系、进行环境调整、管理合股人的资产开销。

从异种数据库集成的角度看，数据仓库也是十分有用的。许多组织收集了形形色色数据，并由多个异种的、自治的、分布的数据源维护大型数据库。集成这些数据，并提供简便、有效的访问是非常希望的，并且也是一种挑战。数据库工业界和研究界都正朝着实现这一目标竭尽全力。

对于异种数据库的集成，传统的数据库做法是：在多个异种数据库上，建立一个包装程序和一个集成程序（或仲裁程序）。这方面的例子包括IBM 的数据连接程序和Informix的数据刀。当一个查询提交客户站点，首先使用元数据字典对查询进行转换，将它转换成相应异种站点上的查询。然后，将这些查询

映射和发送到局部查询处理器。由不同站点返回的结果被集成为全局回答。这种查询驱动的方法需要复杂的信息过滤和集成处理，并且与局部数据源上的处理竞争资源。这种方法是低效的，并且对于频繁的查询，特别是需要聚集操作的查询，开销很大。

对于异种数据库集成的传统方法，数据仓库提供了一个有趣的替代方案。数据仓库使用更新驱动的方法，而不是查询驱动的方法。这种方法将来自多个异种源的信息预先集成，并存储在数据仓库中，供直接查询和分析。与联机事务处理数据库不同，数据仓库不包含最近的信息。然而，数据仓库为集成的异种数据库系统带来了高性能，因为数据被拷贝、预处理、集成、注释、汇总，并重新组织到一个语义一致的数据存储中。在数据仓库中进行的查询处理并不影响在局部源上进行的处理。此外，数据仓库存储并集成历史信息，支持复杂的多维查询。这样，建立数据仓库在工业界已非常流行。

1.操作数据库系统与数据仓库的区别由于大多数人都熟悉商品关系数据库系统，将数据仓库与之比较，就容易理解什么是数据仓库。

联机操作数据库系统的主要任务是执行联机事务和查询处理。这种系统称为联机事务处理（OLTP）系统。它们涵盖了一个组织的大部分日常操作，如购买、库存、制造、银行、工资、注册、记帐等。另一方面，数据仓库系统在数据分析和决策方面为用户或“知识工人”提供服务。这种系统可以用不同的格式组织和提供数据，以便满足不同用户的形形色色需求。这种系统称为联机分析处理（OLAP）系统。

OLTP 和OLAP 的主要区别概述如下。

(1)用户和系统的面向性：OLTP 是面向顾客的，用于办事员、客户、和信息技术专业人员的事务和查询处理。OLAP 是面向市场的，用于知识工人（包括经理、主管、和分析人员）的数据分析。

(2)数据内容：OLTP 系统管理当前数据。通常，这种数据太琐碎，难以方便地用于决策。OLAP 系统管理大量历史数据，提供汇总和聚集机制，并在不同的粒度级别上存储和管理信息。这些特点使得数据容易用于见多识广的决策。

(3)数据库设计：通常，OLTP 系统采用实体-联系（ER）模型和面向应用的数据库设计。而OLAP 系统通常采用星形或雪花模型和面向主题的数据库设计。

(4)视图：OLTP 系统主要关注一个企业或部门内部的当前数据，而不涉及历史数据或不同组织的数据。相比之下，由于组织的变化，OLAP 系统常常跨

越数据库模式的多个版本。OLAP 系统也处理来自不同组织的信息，由多个数据存储集成的信息。由于数据量巨大，OLAP 数据也存放在多个存储介质上。

(5)、访问模式：OLTP 系统的访问主要由短的、原子事务组成。这种系统需要并行控制和恢复机制。然而，对OLAP系统的访问大部分是只读操作（由于大部分数据仓库存放历史数据，而不是当前数据），尽管许多可能是复杂的查询。 OLTP 和OLAP 的其它区别包括数据库大小、操作的频繁程度、性能度量等。

2.但是，为什么需要一个分离的数据仓库“既然操作数据库存放了大量数据”，你注意到，“为什么不直接在这种数据库上进行联机分析处理，而是另外花费时间和资源去构造一个分离的数据仓库？”分离的主要原因是提高两个系统的性能。操作数据库是为已知的任务和负载设计的，如使用主关键字索引和散列，检索特定的记录，和优化“罐装的”查询。另一方面，数据仓库的查询通常是复杂的，涉及大量数据在汇总级的计算，可能需要特殊的数据组织、存取方法和基于多维视图的实现方法。在操作数据库上处理OLAP 查询，可能会大大降低操作任务的性能。

此外，操作数据库支持多事务的并行处理，需要加锁和日志等并行控制和恢复机制，以确保一致性和事务的强健性。通常，OLAP 查询只需要对数据记录进行只读访问，以进行汇总和聚集。如果将并行控制和恢复机制用于这OLAP 操作，就会危害并行事务的运行，从而大大降低OLTP 系统的吞吐

量。

最后，数据仓库与操作数据库分离是由于这两种系统中数据的结构、内容和用法都不相同。决策支持需要历史数据，而操作数据库一般不维护历史数据。在这种情况下，操作数据库中的数据尽管很丰富，但对于决策，常常还是远远不够的。决策支持需要将来自异种源的数据统一（如，聚集和汇总），产生高质量的、纯净的和集成的数据。相比之下，操作数据库只维护详细的原始数据（如事务），这些数据在进行分析之前需要统一。由于两个系统提供很不相同的功能，需要不同类型的数据，因此需要维护分离的数据库。

Data warehousing provides architectures and tools for business executives to sy stematically organize, understand, and use their data to make strategic decisions. A lar ge number of organizations have found that data warehouse systems are valuable tools in today's competitive, fast evolving world. In the last several years, many firms have spent millions of dollars in building enterprise-

wide data warehouses. Many people feel that with competition mounting in every ind ustry, data warehousing is the latest must-have marketing weapon ——

a way to keep customers by learning more about their needs.

“So", you may ask, full of intrigue, “what exactly is a data warehouse?"

Data warehouses have been defined in many ways, making it difficult to formulat e a rigorous definition. Loosely speaking, a data warehouse refers to a database that is maintained separately from an organization's operational databases. Data warehouse s ystems allow for the integration of a variety of application systems. They support info rmation processing by providing a solid platform of consolidated, historical data for a nalysis.

According to W. H. Inmon, a leading architect in the construction of data wareho use systems, “a data warehouse is a subject-oriented, integrated, time-

variant, and nonvolatile collection of data in support of management's decision makin g process." This short, but comprehensive definition presents the major features of a d ata warehouse. The four keywords, subject-oriented, integrated, time-

variant, and nonvolatile, distinguish data warehouses from other data repository syste ms, such as relational database systems, transaction processing systems, and file syste ms. Let's take a closer look at each of these key features.

(1).Subject-

oriented: A data warehouse is organized around major subjects, such as customer, ven dor, product, and sales. Rather than concentrating on the day-to-

day operations and transaction processing of an organization, a data warehouse focuse s on the modeling and analysis of data for decision makers. Hence, data warehouses ty pically provide a simple and concise view around particular subject issues by excludin

g data that are not useful in the decision support process.

(2) Integrated: A data warehouse is usually constructed by integrating multiple he terogeneous sources, such as relational databases, flat files, and on-

line transaction records. Data cleaning and data integration techniques are applied to e nsure consistency in naming conventions, encoding structures, attribute measures, and so on.

(3).Time-

variant: Data are stored to provide information from a historical perspective (e.g., the past 5-

10 years). Every key structure in the data warehouse contains, either implicitly or expl icitly, an element of time.

(4)Nonvolatile: A data warehouse is always a physically separate store of data tra

nsformed from the application data found in the operational environment. Due to this separation, a data warehouse does not require transaction processing, recovery, and co ncurrency control mechanisms. It usually requires only two operations in data accessi ng: initial loading of data and access of data.

In sum, a data warehouse is a semantically consistent data store that serves as a p hysical implementation 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 heterogeneou s sources to support structured and/or ad hoc queries, analytical reporting, and decisio n 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 integratio n, data cleaning, and data consolidation. The utilization of a data warehouse often nec essitates a collection of decision support technologies. This allows “knowledge worke rs" (e.g., managers, analysts, and executives) to use the warehouse to quickly and con veniently obtain an overview of the data, and to make sound decisions based on infor mation in the warehouse. Some authors use the term “data warehousing" to refer only to the process of data warehouse construction, while the term warehouse DBMS is use d to refer to the management and utilization of data warehouses. We will not make thi s distinction here.

“How are organizations using the information from data warehouses?" Many org anizations are using this information to support business decision making activities, in cluding:

(1) increasing customer focus, which includes the analysis of customer buying pa tterns (such as buying preference, buying time, budget cycles, and appetites for spendi ng),

(2) repositioning products and managing product portfolios by comparing the per formance of sales by quarter, by year, and by geographic regions, in order to fine-tune 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 dat

abase integration. Many organizations typically collect diverse kinds of data and main tain large databases from multiple, heterogeneous, autonomous, and distributed infor mation sources. To integrate such data, and provide easy and efficient access to it is hi ghly desirable, yet challenging.

Much effort has been spent in the database industry and research community tow ards achieving this goal.

The traditional database approach to heterogeneous database integration is to buil d 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 quer y is posed to a client site, a metadata dictionary is used to translate the query into quer ies appropriate for the individual heterogeneous sites involved. These queries are then mapped and sent to local query processors. The results returned from the different sit es are integrated into a global answer set. This query-

driven approach requires complex information filtering and integration processes, and competes for resources with processing at local sources. It is inefficient and potentiall y expensive for frequent queries, especially for queries requiring aggregations.

Data warehousing provides an interesting alternative to the traditional approach o f heterogeneous database integration described above. Rather than using a query-driven approach, data warehousing employs an update-

driven approach in which information from multiple, heterogeneous sources is integra ted in advance and stored in a warehouse for direct querying and analysis. Unlike on-line transaction processing databases, data warehouses do not contain the most current information. However, a data warehouse brings high performance to the integrated he terogeneous database system since data are copied, preprocessed, integrated, annotate d, summarized, and restructured into one semantic data store. Furthermore, query proc essing in data warehouses does not interfere with the processing at local sources. Mor eover, data warehouses can store and integrate historical information and support com plex multidimensional queries. As a result, data warehousing has become very popula r in industry.

1. Differences between operational database systems and data warehouses

Since most people are familiar with commercial relational database systems, it is easy to understand what a data warehouse is by comparing these two kinds of systems .

The major task of on-line operational database systems is to perform on-

line transaction and query processing. These systems are called on-

line transaction processing (OLTP) systems. They cover most of the day-to-

day operations of an organization, such as, purchasing, inventory, manufacturing, ban king, 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 organize and present data in various formats in order to accommod ate the diverse needs of the different users. These systems are known as on-

line analytical processing (OLAP) systems.

The major distinguishing features between OLTP and OLAP are summarized as f ollows.

(1). Users and system orientation: An OLTP system is customer-

oriented and is used for transaction and query processing by clerks, clients, and infor mation technology professionals. An OLAP system is market-

oriented and is used for data analysis by knowledge workers, including managers, exe cutives, 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 amou nts of historical data, provides facilities for summarization and aggregation, and stores and manages information at different levels of granularity. These features make the d ata easier for use in informed decision making.

(3). Database design: An OLTP system usually adopts an entity-

relationship (ER) data model and an application -

oriented database design. An OLAP system typically adopts either a star or snowflake model, and a subject-oriented database design.

(4). View: An OLTP system focuses mainly on the current data within an enterpri se or department, without referring to historical data or data in different organizations. In contrast, an OLAP system often spans multiple versions of a database schema, due to the evolutionary process of an organization. OLAP systems also deal with informat ion that originates from different organizations, integrating information from many da ta stores. Because of their huge volume, OLAP data are stored on multiple storage me dia.

(5). Access patterns: The access patterns of an OLTP system consist mainly of sh ort, atomic transactions. Such a system requires concurrency control and recovery me chanisms. However, accesses to OLAP systems are mostly read-

only operations (since most data warehouses store historical rather than up-to-

date information), although many could be complex queries.

Other features which distinguish between OLTP and OLAP systems include data base size, frequency of operations, and performance metrics and so on. 2. But, why ha ve a separate data warehouse?

“Since operational databases store huge amounts of data", you observe, “why not perform on-

line analytical processing directly on such databases instead of spending additional ti me 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 w orkloads, such as indexing and hashing using primary keys, searching for particular re cords, and optimizing “canned" queries. On the other hand, data warehouse queries ar e often complex. They involve the computation of large groups of data at summarized levels, and may require the use of special data organization, access, and implementati on methods based on multidimensional views. Processing OLAP queries in operationa l databases would substantially degrade the performance of operational tasks.

Moreover, an operational database supports the concurrent processing of several t ransactions. Concurrency control and recovery mechanisms, such as locking and loggi ng, are required to ensure the consistency and robustness of transactions. An OLAP qu ery often needs read-

only access of data records for summarization and aggregation. Concurrency control a nd recovery mechanisms, if applied for such OLAP operations, may jeopardize the ex ecution 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 mainta in historical data. In this context, the data in operational databases, though abundant, i s usually far from complete for decision making. Decision support requires consolidat ion (such as aggregation and summarization) of data from heterogeneous sources, resu lting in high quality, cleansed and integrated data. In contrast, operational databases c ontain only detailed raw data, such as transactions, which need to be consolidated bef ore analysis. Since the two systems provide quite different functionalities and require

different kinds of data, it is necessary to maintain separate databases.