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Big Island, HI, USA
Jan. 6, 2005 to Jan. 6, 2005
ISBN: 0-7695-2268-8
pp: 293c
John Cheng , Global InfoTek
Ray Emami , Global InfoTek
Larry Kerschberg , KRM, Inc
Eugene Santos, Jr,. , University of Connecticut
Qunhua Zhao , University of Connecticut
Hien Nguyen , University of Connecticut
Hua Wang , University of Connecticut
Michael Huhns , University of South Carolina
Marco Valtorta , University of South Carolina
Jiangbo Dang , University of South Carolina
Hrishikesh Goradia , University of South Carolina
Jingshan Huang , University of South Carolina
Sharon Xi , University of South Carolina
This paper describes the current state of the OmniSeer system. OmniSeer supports intelligence analysts in the handling of massive amounts of data, the construction of scenarios, and the management of hypotheses. OmniSeer models analysts with dynamic user models that capture an analyst's context, interests, and preferences, thus enabling more efficient and effective information retrieval. OmniSeer explicitly represents the prior and tacit knowledge of analysts, thus enabling transfer and reuse of such knowledge. Both the user and cognitive models employ a Bayesian network fragment representation, which supports principled probabilistic reasoning and analysis. An independent evaluation of OmniSeer was carried out at NIST and will be used to guide further development.
John Cheng, Ray Emami, Larry Kerschberg, Eugene Santos, Jr,., Qunhua Zhao, Hien Nguyen, Hua Wang, Michael Huhns, Marco Valtorta, Jiangbo Dang, Hrishikesh Goradia, Jingshan Huang, Sharon Xi, "OmniSeer: A Cognitive Framework for User Modeling, Reuse of Prior and Tacit Knowledge, and Collaborative Knowledge Services", HICSS, 2005, Proceedings of the 38th Annual Hawaii International Conference on System Sciences, Proceedings of the 38th Annual Hawaii International Conference on System Sciences 2005, pp. 293c, doi:10.1109/HICSS.2005.462
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