Applied Mechanics and Materials Online: 2013-08-30 ISSN: 1662-7482, Vols. 380-384, pp 4796-4799 doi:10.4028/www.scientific.net/amm.380-384.4796 2013 Trans Tech Publications, Switzerland Construction of the Library Management System Based on Data Warehouse and OLAP Maoli Xu 1, a, Xiuying Li 2,b 1 Jilin Agricultural University library, 130118, Changchun, China 2 Information Teaching and Management Center, Jilin Agricultural University, Changchun, China a zqs19741030@126.com, b 605816479@qq.com Keywords: data warehouse; OLAP; data mining; Management System Abstract. Along with the evolvement of data warehouse technology, the establishment of the decision support system for libraries is becoming more and more important. This paper provides the architecture of the data warehouse-based library decision support system and focuses on discussing the model design and implementation technology of data warehouse building. Introduction Along with the progress in library-related technologies in recent years, the information processing technology has also achieved rapid growth, which makes information retrieval and searching much easier to realize. With the number of books growing at such a speed and their variety so diversified, better data management technology is needed because the current computer processing capacity with traditional database cannot accomplish such kind of processing. When managers cannot extract more useful, convenient and decision-facilitating information from the traditional database processing, the data warehouse technology emerges at the right moment. Tab.1 Comparison Table of Traditional Transactional Database and Data Warehouse Comparison Items Database Data Warehouse Data Features Dynamic change Static; can only be added at fixed time Data Content Current data Historical and archived data Data Target Deal with concrete transactions Provide decision support Operation Frequency High Low Data Visits Response Time Data Warehouse Few Very short, calculated in microseconds or seconds Some transactions might need to access a large amount of data Unit of measurement is not fixed Data warehouse is generally a data set that is theme-oriented, integrated, ever-changing and also relatively stable. It is used for supporting the administrative decision-making process and its true value lies in deep data mining, multi-dimensional data analysis, and dynamic statement query to help people make decisions conducive to improving the workflow, instead of being confined to the workflow automation [1]. It is thus clear that data warehouse has a qualitative leap against the relational database system used in the normal MIS. The Tab.1 reflects the simple comparison between traditional transactional database and data warehouse. All rights reserved. No part of contents of this paper may be reproduced or transmitted in any form or by any means without the written permission of Trans Tech Publications, www.ttp.net. (ID: 130.203.136.75, Pennsylvania State University, University Park, USA-11/05/16,05:13:15)
Applied Mechanics and Materials Vols. 380-384 4797 Architecture of Library Data Warehouse The architecture of library data warehouse is divided into three parts, i.e. data source, data warehouse and front-end analytical application, as shown in Fig.1. User University Library Decision Support System decision c Front-end Analytical Application Layer Data Mining OLAP Data Warehouse Data Warehouse Layer Data Extraction,Conversion, Loading Documentation Operational Database Other Data Data Source Layer Fig.1 System Architecture Data Source Layer: Get data information (both internal and external) from various information sources. Internal data source refers to the backend database of library management system. External data includes various information sources, data information on the web portals of all kinds of subjects and also some business data, all for the use of analysis. Data source is where the data of data warehouse comes from and provides source data for the library decision support system [2]. Data Warehouse Layer: Extract, purify and convert the data obtained from various information sources according to certain rules, and re-combine the data into an overall data view. Then store such data view in the data warehouse and conduct management and maintenance for it. Therefore, this layer includes three parts, i.e. data acquisition, storage and management. Front-end Analytical Application Layer: It is established on the basis of data warehouse and mainly composed of OLAP and data mining. With the support of various analytical and mining tools, decision makers can make the right decisions. The knowledge discovered by data mining can be directly used for guiding the OLAP, while the new knowledge obtained by data mining and OLAP can also replenish the system s knowledge base immediately. The library data warehouse has a large amount of data and certainly contains some potential rules and knowledge. Such rules and knowledge are hard to be discovered with traditional analytical methods, but data mining technology provides a strategy for this problem, namely get to know the relevance between a certain kind of readers and a certain kind of books by digging into the relationship between readers and books, thus supporting the decision as to book purchasing [3]. The advantages of data warehouse make up for the defects of DSS to make the best of the database resources in the system and make the overall system become an organic whole, thus enhancing the system integration. Design and Implementation of System Design of Data Warehouse Subject Confirmation: Clearly clarify the users demand and have an in-depth understanding and analysis of the demand of managers, especially decision makers, and then confirm several subjects according to the degree of urgency and importance of such demand. In line with the business
4798 Vehicle, Mechatronics and Information Technologies requirements of university libraries and the objectives of system construction, the decision-making subjects in the decision support system of university libraries include reader demand analysis, book purchasing decision-making, analysis of library collection structure, book loan analysis, and book circulation analysis [4]. Conceptual Design: Conceptual model serves as the bridge linking subjectivity and objectivity. The matters in the objective world can be specifically described by using the model and language suitable for the computer world. Information package is composed of facts, dimensionality and granularity. The information package of reader demand analysis is as shown in Fig.2. Loan Date Reader Books Loan Place Year Type Primary Classification Month Unit Secondary Classification Day Grade Publication Year Hour Gender Press Measure indexes: number of readers, average books per student, average quantity of buying books per student, number of book loans, average books loaned by each student, and demand proportion Fig.2 Information Package of Reader Demand Analysis Logic Design: The design of logic model is very critical to the data warehouse implementation. It can directly reflect the demand of the operating department and has an important guiding role in a system s physical implementation. This system adopts the star schema to establish the logic model of data warehouse. The centre of the star schema is a large fact table with no redundancy, with a group of small subsidiary tables (called as dimension tables) around it. System Implementation Technology SQL Server 2000: SQL Server is a database management system with complete functions, including development-supporting engine, standard SQL language, extensibility (such as replication, OLAP and analysis), etc. Features like stored procedure and trigger are only owned by large database [5]. ASP technology is actually a set of server-side scripting environment developed by Microsoft, included in IIS3.0 and 4.0. With ASP, we can combine HTML webpage, ASP instruction and ActiveX component to establish dynamic, interactive and efficient WEB server applications. Fig.3 shows the library management system model
Applied Mechanics and Materials Vols. 380-384 4799 User 1 web server Knowledge Server Knowledge base User 2 campus net decision information data mining OLAP web data warehouse Integrated Data User n Sample data Data acquisition unit Historical data Conclusion Fig.3 Library management system model The research and development of the data warehouse-based decision support system of university libraries can effectively address the contradiction between the existing huge data and the data analysis and support the library decision-making, management and services. The statistical analysis and deep mining of a large amount of library data can lead to the law of library information resource utilization and infrastructure utilization, effectively improve the book circulation, quality structure of book collections and various information services, better serve readers, and fully utilize the information stored in libraries to serve the management layer to better support the decision-making. References client Data server data warehouse [1] Hu Min. Data warehouses and library decision support systems[j].library and information service, 2002(1): 72-88. [2] Liu Jing. Design and implementation of university library manage basing on data warehouse[j]. Library and information service, 2009(8): 126-128. [3] Zi Yun, Li Yipeng. On book purchasing decision system of university libraries based on data warehouse[j]. New century library, 2006(4):12-14. [4] Xiong Yongjun, Chen Chunying. Digital resources service analysis system based on data warehouse and OLAP[J] Journal of modern information, 2009(11): 153-157. [5] Zhang Hongwu. Research on the application for data warehouse of university library[j]. Journal of weinan teachers university, 2011(12): 80-84.
Vehicle, Mechatronics and Information Technologies 10.4028/www.scientific.net/AMM.380-384 Construction of the Library Management System Based on Data Warehouse and OLAP 10.4028/www.scientific.net/AMM.380-384.4796