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New Technology of Library and Information Service  2008, Vol. 24 Issue (2): 48-52    DOI: 10.11925/infotech.1003-3513.2008.02.09
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Framework of Just-In-Time Information Retrieval Based on Cooperation of Multi Aspect
Chen Honggang   Zhuang Chao
(Department of Computer Science, Huazhong Normal University, Wuhan 430079, China)
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A design framework about Just-In-Time Information Retrieval (JITIR) is proposed based on cooperation of multi aspect, including cooperation of DM and KDD, cooperation of agents, cooperation of vectors respectively. In this framework, the buffer knowledge database is added to and communication of agents is paid attention to, at the same time the interesting vector and outcome vector are also used. After analyzing the framework, the authors conclude that precision can be improved.

Key wordsJust-In-Time information retrieval      Agent      Vector      Knowledge discovery in database      Cooperation     
Received: 29 October 2007      Published: 25 February 2008


Corresponding Authors: Chen Honggang     E-mail:
About author:: Chen Honggang,Zhuang Chao

Cite this article:

Chen Honggang,Zhuang Chao. Framework of Just-In-Time Information Retrieval Based on Cooperation of Multi Aspect. New Technology of Library and Information Service, 2008, 24(2): 48-52.

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[1] Bradley Rhodes. Using Physical Context for Just-In-Time Information Retrieval[J].  IEEE Transactions on Computers,  2003,52(8):1011-1014.
[2] Bradley James Rhodes. Just-In-Time Information Retrieval[D]. MIT,2000.
[3] 杨炳儒,李晋宏,宋威,等. 面向复杂系统的知识发现过程模型KD(D&K)及其应用[J]. 自动化学报,2007, 37(2):151-155.
[4] Yan G B, Zhang T H, Coordinators Based  on Cognitive Psychology Features and the Corresponding KDD Process Model (in English)[J].  中国科学技术大学学报,2007,37(2):212-216.
[5] Tao Li,Chang-shing Perng.KDD2006 Workshop Report  Theory and Practice of Temporal Data Mining[J].  ACM  SIGKDD Explorations,2006,8(2):96-97.
[6] LanH.Witten.数据挖掘:实用机器学习技术[M]. 第2版.北京:机械工业出版社,2005:253-267.
[7] 杨炳儒.基于内在基理的知识发现理论及其应用[M]. 北京:电子工业出版社,2004:78-91.
[8] UMBC Agent Web[EB/OL]. [2007-09-10].

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