Generating rules from data mining for collaboration moderator services

Authors:
C. Palmer;J. A. Harding;R. Swarnkar;B. P. Das;R. I. Young
Affiliations:
Wolfson School of Mechanical and Manufacturing Engineering, Loughborough University, Leicestershire, UK LE11 3TU;Wolfson School of Mechanical and Manufacturing Engineering, Loughborough University, Leicestershire, UK LE11 3TU;Wolfson School of Mechanical and Manufacturing Engineering, Loughborough University, Leicestershire, UK LE11 3TU;Wolfson School of Mechanical and Manufacturing Engineering, Loughborough University, Leicestershire, UK LE11 3TU;Wolfson School of Mechanical and Manufacturing Engineering, Loughborough University, Leicestershire, UK LE11 3TU
Venue:
Journal of Intelligent Manufacturing
Year:
2013

Citing 5
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A framework for collaboration moderator services to support knowledge based collaboration

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Abstract

A Moderator is a knowledge based system that supports collaborative working by raising awareness of the priorities and requirements of other team members. However, the amount of advice a Moderator can provide is limited by the knowledge it contains on team members. The use of data mining techniques can contribute towards automating the process of knowledge acquisition for a Moderator and enable hidden data patterns and relationships to be discovered to facilitate the moderation process. A novel approach is presented, consisting of a knowledge discovery framework which provides a semi-automatic methodology to generate rules by inserting relationships discovered as a result of data mining into a generic template. To demonstrate the knowledge discovery framework methodology an application case is described. The application case acquires knowledge for a Moderator to make project partners aware of how to best formulate a proposal for a European research project by data mining summaries of successful past projects. Findings from the application case are presented.