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1、个人资料整理仅限学习使用 河北工程大学毕业论文 面向主题的：数据仓库围绕一些主题，如顾客、供应商、产品和销售组 织。数据仓库关注决策者的数据建模与分析，而不是构造组织机构的日常操作 和事务处理。因此，数据仓库排除对于决策无用的数据，提供特定主题的简明 视图。 (2集成的：通常，构造数据仓库是将多个异种数据源，如关系数据库、一 论文题目：鸿海种业仓库管理系统的 设计与实现 作者姓名：石成华 专业班级：信管 1001 学号信息： 100340119 指导老师：张贵炜 论文日期： 2018.04.10 个人资料整理仅限学习使用 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
2、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 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
3、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 ” 、增加顾客关注，包括分析顾客购买模式、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
4、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。
5、相比之下，由于组织的变化，OLAP 系统常常跨 个人资料整理仅限学习使用 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.Subject- oriented: A data warehouse is organized around major subjects, such as customer, ven dor, product, and sales
6、. 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 excludi
7、n g data that are not useful in the decision support process. (2 Integrated: A data warehouse is usually constructed by integrating multiple h eterogeneous sources, such as relational databases, flat files, and on- line transaction records. Data cleaning and data integration techniques are applied t
8、o 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 exp licitly, a
9、n element of time. (4Nonvolatile: A data warehouse is always a physically separate store of data tr 个人资料整理仅限学习使用 ansformed from the application data found in the operational environment. Due to this separation, a data warehouse does not require transaction processing, recovery, and c oncurrency cont
10、rol mechanisms. It usually requires only two operations in data access ing: 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 e
11、nterprise 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
12、 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 decisi
13、on 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 re
14、fer 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 thi
15、s information to support business decision making activities, in cluding: (1 increasing customer focus, which includes the analysis of customer buying p atterns (such as buying preference, buying time, budget cycles, and appetites for spen ding, (2 repositioning products and managing product portfol
16、ios by comparing the pe rformance 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 corpo
17、rate assets. Data warehousing is also very useful from the point of view of heterogeneous dat 个人资料整理仅限学习使用 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 in
18、tegrate 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
19、 and integrators (or mediators on top of multiple, heterogeneous database s. A variety of data joiner and data blade products belong to this category. When a que ry is posed to a client site, a metadata dictionary is used to translate the query into que ries appropriate for the individual heterogene
20、ous sites involved. These queries are the n mapped and sent to local query processors. The results returned from the different si tes are integrated into a global answer set. This query- driven approach requires complex information filtering and integration processes, and competes for resources with
21、 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
22、 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 contai
23、n 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
24、 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 a
25、nd 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- 个人资料整理仅限学习使用 line transaction and query
26、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 user
27、s 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 distinguishin
28、g 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
29、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 summarizatio
30、n 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.
31、 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 sy
32、stem 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 mu
33、ltiple 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- 个人资料整理仅限学习使用 only operations (since most data wareh
34、ouses 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 o
35、perational 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 perfo
36、rmance 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 c
37、omputation 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. More
38、over, 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 fo
39、r 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 ware
40、houses 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
41、from complete for decision making. Decision support requires consolidat ion (such as aggregation and summarization of data from heterogeneous sources, res ulting in high quality, cleansed and integrated data. In contrast, operational databases contain only detailed raw data, such as transactions, which need to be consolidated be fore analysis. Since the two systems provide quite different functionalities and require 个人资料整理仅限学习使用 different kinds of data, it is necessary to maintain separate databases.