Information supply: An approach based on demand modelling and information filtering-Reference-Cited by-同舟云学术

Information supply: An approach based on demand modelling and information filtering

Published:2008-04-01 Issue:4 Volume:222 Page:541-557
ISSN:0954-4054
Container-title:Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture
language:en
Short-container-title:Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture

Author:

Zhen L¹,Jiang Z¹,Song H²,Liu C¹,Liang J¹

Affiliation:

1. School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, People's Republic of China

2. School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, People's Republic of China

Abstract

Collaborative team members usually come from diverse disciplines; their demands for information are also different from each other. This paper is mainly concerned with how to capture designers’ demands for information in a collaborative team. From a workflow perspective, designers’ information demand is modelled considering three aspects: members’, roles’, and tasks’ requirements for information. Based on a static model of information demand and information filtering technologies, a dynamic model is proposed so that the designers’ information demand could be derived automatically. In addition, the appropriate volume of information for designers’ needs could also be determined intelligently. With the information demand model, an information supply system could be developed to realize the following objective: the delivery of an appropriate volume of information within an appropriate domain, delivered to the appropriate user in the collaborative team.

Publisher

SAGE Publications

Subject

Industrial and Manufacturing Engineering,Mechanical Engineering

Link

http://journals.sagepub.com/doi/pdf/10.1243/09544054JEM891

Reference30 articles.

1. Knowledge logistics in information grid environment

2. Ontological user profiling in recommender systems

3. Web mining model and its applications for information gathering

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