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Supporting the optimisation of distributed data mining by predicting application run times
Monash University, Melbourne, VIC.
Monash University, Melbourne, VIC.
2002 (English)In: Proceedings of the Fourth International Conference on Enterprise Information Systems : Ciudad Real, Spain, April 3 - 6, 2002 / ICEIS 2000, INSTICC Press, 2002, 374-381 p.Conference paper (Refereed)
Abstract [en]

There is an emerging interest in optimisation strategies for distributed data mining in order to improve response time. Optimisation techniques operate by first identifying factors that affect the performance in distributed data mining, computing/assigning a "cost" to those factors for alternate scenarios or strategies and then choosing a strategy that involves the least cost. In this paper we propose the use of application run time estimation as solution to estimating the cost of performing a data mining task in different distributed locations. A priori knowledge of the response time provides a sound basis for optimisation strategies, particularly if there are accurate techniques to obtain such knowledge. In this paper we present a novel rough set based technique for predicting the run times of applications. We also present experimental validation of the prediction accuracy of this technique for estimating the run times of data mining tasks

Place, publisher, year, edition, pages
INSTICC Press, 2002. 374-381 p.
URN: urn:nbn:se:ltu:diva-40010Local ID: ef917940-d584-11dc-958e-000ea68e967bISBN: 972-98050-6-7 (print)OAI: diva2:1013532
International Conference on Enterprise Information Systems : 03/04/2002 - 06/04/2002
Upprättat; 2002; 20080207 (cira)Available from: 2016-10-03 Created: 2016-10-03

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