ACM Home Page
Please provide us with feedback. Feedback
Task-based information management
Full text PdfPdf (221 KB)
Source ACM Computing Surveys (CSUR) archive
Volume 31 ,  Issue 2es  (June 1999) table of contents
Article No. 10  
Year of Publication: 1999
ISSN:0360-0300
Author
Michael Wolverton  SRI International, Menlo Park, CA
Publisher
ACM  New York, NY, USA
Bibliometrics
Downloads (6 Weeks): 10,   Downloads (12 Months): 66,   Citation Count: 0
Additional Information:

abstract   references   index terms   collaborative colleagues   peer to peer  

Tools and Actions: Review this Article  
Save this Article to a Binder    Display Formats: BibTex  EndNote ACM Ref   
DOI Bookmark: Use this link to bookmark this Article: http://doi.acm.org/10.1145/323216.323357
What is a DOI?

ABSTRACT

Effective collaboration in fast-changing environment can put great dem ands on a collaborator's time. Therefore, information retrieval and filtering tools for these environments should impose as little on that time as possible. Not only should they exclude as many irrelevant documents as possible from those presented to the user (to avoid the time wasted sorting through and reading those documents), they should also minimize the user's effort in characterizing his or her information needs. The goal of the Task-based Information Distribution Environment (TIDE) system is to achieve these objectives by explicitly representing each collaborator's current task and using those representations to deliver documents that meet the information needs implied by those tasks. It does this by treating information gathering as a diagnosis problem, in which the situation (i.e., the current state of beliefs about various questions related to a task) leads probabilistically to test that will provide the most evidence toward reaching a diagnosis (i.e., a description of the documents most likely to be useful to that task). It encodes tasks as nodes in a Bayesian network, and computes document descriptions based on the probabilistic relationship among tasks and their corresponding information requirements.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

1
 
2
 
3
Friedman, N. and Goldszmidt, M. 1996a. Building classiers using Bayesian networks. In Proceedings of the Thirteenth National Conference on Articial Intelligence (AAAI'96) (1996).
 
4
Friedman, N. and Goldszmidt, M. 1996b. Learning Bayesian networks with local structure. In Proc. Twelfth Conference on Uncertainty in Articial Intelligence (UAI'96) (1996).
 
5
McGuinness, D. 1998. Ontological issues for knowledge-enhanced search. In Proceedings of Formal Ontology in Information Systems (1998).
 
6
 
7
 
8
Yan, T. W. and Garcia-Molina, H. 1995. SIFT|a tool for wide-area information dissemination. InProceedings of the 1995 USENIX Technical Conference (1995), pp. 177-186.


Peer to Peer - Readers of this Article have also read: