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New Technology of Library and Information Service  2010, Vol. 26 Issue (9): 28-36    DOI: 10.11925/infotech.1003-3513.2010.09.06
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Research and Implementation of Classification System of E-government Information Resources Based on Business Association
Qiao Jianzhong
National Science Library, Chinese Academy of Sciences, Beijing 100190, China;
Educational Technology Center of PLA Academy of Arts, Beijing 100081, China
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In this paper, an E-government information classification system of B/S structure is designed and implemented based on MVC mode. The author describes the system architecture and business logic, with emphasis on four key technologies including data abstraction, business association, access control and visualization. The system is proven to meet the functional requirements of the classification mechanism and business association, and the visualization of the whole classification structure and the correlations between business categories is realized.

Key wordsE-government      Information      resources      Classification      system      Business      association      Implicit      information      correlation      Information      visualization      MVC     
Received: 23 June 2010      Published: 26 October 2010



Cite this article:

Qiao Jianzhong. Research and Implementation of Classification System of E-government Information Resources Based on Business Association. New Technology of Library and Information Service, 2010, 26(9): 28-36.

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[1] DB11/Z 359-2006,面向公共服务的政务信息分类规范

[2] 陈拂晓.深度解析《国家电子政务总体框架》 .计算机世界,2007-01-29(B05).

[3] Marchand D A,Kresslein J C. Information Resources Management and the Public Administrator //Rabin J, Jackowski E M. Handbook of Information Resources Management
[M].Marcel Dekker Inc., 1988: 395-455.

[4] Bouguettaya A, Elmagarmid, A K, Medjahed B, et al. Ontologybased Support for Digital Government . In:Proceedings of the 27th International Conference on Very Large Data Bases. San Francisco, CA, USA:Morgan Kaufmann Publishers,2001: 633-636.

[5] 李锋白.政务信息资源目录体系和交换体系 推动部门间信息共享和业务协同 . .中国计算机报,2007(8). 77_1.html.

[6] 谢先江.区域性电子政务信息资源目录体系实现研究
[J]. 电子政务 ,2007(12):37-42.

[7] 程建华,栾婕,陈玉龙.政务信息资源交换体系的统一平台设计
[J]. 计算机应用研究 ,2007,24(9):228-230.

[8] Stoll M, Laner D. Service Oriented E-Government .Sobh T M, Elleithy K M.Innovations in Computing Sciences and Software Engineering
[M].New York:Springer, 2010:129-134.

[9] 刘寅斌.基于Petri网的政务信息资源管理模型的研究
[J]. 扬州大学学报:自然科学版 , 2008, 11(4):46-48.

[10] 李志刚,彭立.基于数据仓库的政务辅助决策支持系统构建研究
[J]. 中国管理信息化:综合版 ,2007(12):85-87.

[11] The Apache Software Foundation . .

[12] Java Excel APIA Java API to Read, Write, and Modify Excel Spreadsheets . .

[13] Prefuse . .

[14] Welcome to DOM4J 2.0 . .

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