【導讀】隨著社會的發(fā)展，計算機已經(jīng)成為人們?nèi)粘Ｉ?，學習辦公中不可缺少的一部分，并在各個領域發(fā)揮著越來越重要的作用。在計算機迅猛發(fā)展的影響下，計算機游戲也隨。拼圖游戲就是其中一種，拼圖游戲適用范圍很廣，老少皆宜?？慑憻拕邮帜芰?、觀察能力，而且還可培養(yǎng)人與人之間的協(xié)作能力。拼圖游戲可分為擺放式和挪動式兩種類型。戲，設計一種在計算機上運行的簡單的挪動式拼圖游戲。本拼圖游戲以Delphi為。開發(fā)軟件在計算機上實現(xiàn)拼圖游戲規(guī)則，游戲可以加載圖片信息，選擇圖片進行游戲，游戲中可以記錄游戲的步數(shù)、時間等，也可以在游戲中導出圖片的原圖作為參考。設計達到了預期的目標。

　　

【正文】 [4] 王棟 delphi 課程設計 [M] 清華大學出版社 2020 年 4 月 [5] 田更 程序設計實例訓教程 [M]科學出版社 2020 年 3 月 [6] 王興晶 尹立宏 delphi 應用編程 150 例 [M] 電子工業(yè)出版社 2020 年 3 月 [7] 于繁華 delphi 基礎教程 [M] 中國水利水電出版社 2020 年 8 月 太原科技大學華科學院學士學位論文 36 附錄 Ⅰ DATA WAREHOUSING Data warehousing provides architectures and tools for business executives to systematically anize, understand, and use their data to make strategic decisions. A large number of anizations have found that data warehouse systems are valuable tools in today39。s petitive, fast evolving world. In the last several years, many firms have spent millions of dollars in building enterprisewide data warehouses. Many people feel that with petition mounting in every industry, data warehousing is the latest musthave 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 formulate a rigorous definition. Loosely speaking, a data warehouse refers to a database that is maintained separately from an anization39。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。s decision making process. This short, but prehensive definition presents the major features of a data warehouse. The four keywords, subjectoriented, integrated, timevariant, and nonvolatile, distinguish data warehouses from other data repository systems, such as relational database systems, transaction processing systems, and file systems. Let39。s take a closer look at each of these key features. (1).Subjectoriented: A data warehouse is anized around major subjects, such as customer, vendor, product, and sales. Rather than concentrating on the daytoday operations and transaction processing of an anization, a data warehouse focuses on the modeling and analysis of data for decision makers. Hence, data warehouses typically provide a simple and concise view around particular subject issues by excluding data that are not useful in the decision support process. (2) Integrated: A data warehouse is usually constructed by integrating multiple heterogeneous sources, such as relational databases, flat files, and online transaction records. Data cleaning and data integration techniques are applied to ensure consistency in naming conventions, encoding structures, attribute measures, and so on. (3).Timevariant: Data are stored to provide information from a historical perspective (., the past 510 years). Every key structure in the data warehouse contains, either implicitly or explicitly, an element of time. (4)Nonvolatile: A data warehouse is always a physically separate store of data transformed from the application data found in the operational environment. Due to this separation, a data warehouse does not require transaction processing, recovery, and concurrency control mechanisms. It usually requires only two operations in data accessing: initial loading of data and access of data. In sum, a data warehouse is a semantically consistent data store that serves as a physical 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 heterogeneous sources to support structured and/or ad hoc queries, analytical reporting, and decision making. 太原科技大學華科學院學士學位論文 37 “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 integration, data cleaning, and data consolidation. The utilization of a data warehouse often necessitates a collection of decision support technologies. This allows “knowledge workers (., managers, analysts, and executives) to use the warehouse to quickly and conveniently obtain an overview of the data, and to make sound decisions based on information 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 used to refer to the management and utilization of data warehouses. We will not make this distinction here. “How are anizations using the information from data warehouses? Many anizations are using this information to support business decision making activities, including: (1) increasing customer focus, which includes the analysis of customer buying patterns (such as buying preference, buying time, budget cycles, and appetites for spending), (2) repositioning products and managing product portfolios by paring the performance of sales by quarter, by year, and by geographic regions, in order to finetune 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 database integration. Many anizations typically collect diverse kinds of data and maintain large databases from multiple, heterogeneous, autonomous, and distributed information sources. To integrate such data, and provide easy and efficient access to it is highly desirable, yet challenging. Much effort has