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Application run time estimation: a quality of service metric for web-based data mining services

Published:11 March 2002Publication History

ABSTRACT

The emergence of Application Service Providers (ASP) hosting Internet-based data mining services is being seen as a viable alternative for organisations that value their knowledge resources but are constrained by the high cost of data mining software. Response time is an important Quality of Service (QoS) metric for web-based data mining service providers. The ability to estimate the response time of data mining algorithms apriori benefits both clients and service providers. The advantage for the clients is that it helps to impose QoS constraints on the service level agreements and the benefit for the service-providers is that it facilitates optimising resource utilisation and scheduling. In this paper we present a novel rough sets based technique for identifying similarity templates to estimate application run times. We also present experimental results and analysis of this technique.

References

  1. digiMine --- URL: http://www.digiMine.comGoogle ScholarGoogle Scholar
  2. Downey,A,B., (1997), "Predicting Queue Times on Space-Sharing Parallel Computers", Proceedings of the Eleventh International Parallel Processing Symposium (IPPS), Geneva, Switzerland, April. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Gibbons,R., (1997), "A Historical Application Profiler for Use by Parallel Schedulers", Lecture Notes in Computer Science (LNCS), 1291, Springer-Verlag, pp.58-75. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Hu,X., (1995), "Knowledge Discovery in Databases: An Attribute-Oriented Rough sets Approach", PhD Thesis. University of Regina, Canada. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. Information Discovery - URL: http://www.datamine.aa.psiweb.comGoogle ScholarGoogle Scholar
  6. Komorowski,J., Pawlak,Z., Polkowski,L., and Skowron, A., (1998), "Rough sets: A Tutorial", in Rough-Fuzzy Hybridization: A New Trend in Decision Making, (eds) S. K. Pal and A. Skowron, Springer-Verlag, pp. 3-98.Google ScholarGoogle Scholar
  7. Krishnaswamy,S., Zaslavsky, A., and Loke,S,W., (2001), "Towards Data Mining Services on the Internet with a Multiple Service Provider Model: An XML Based Appraoch", Journal of Electronic Commerce Research-Special Issue on Electronic Commerce and Service Operations, Vol. 2, Number 3, http://www.csulb.au/journals/jecrGoogle ScholarGoogle Scholar
  8. Pawlak,Z., (1992), "Rough sets: Theoretical Aspects of Reasoning about Data", Kluwer Academic Publishers, London. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. Sahai,A., Ouyang, J., Machiraju,V., and Werster, K., (2001), "BizQoS: specifying and Gauranteeing Quality of Service for Web Services through Real Time Measurement and Adaptive Control", Hewlett-Packard Labs Technical Report HPL-2001-96, http://www.hpl.hp.com/techreports/2001/HPL-2001-134.htmlGoogle ScholarGoogle Scholar
  10. Sarawagi,S., and Nagaralu,S,H., (2000), "Data Mining Models as Services on the Internet", SIGKDD Explorations, Vol. 2, Issue. 1, http://www.acm.org/sigkdd/explorations/ Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. Smith,W., Foster, I, and Taylor, V., (1998), "Predicting Application Run Times Using Historical Information", in Proc. of the IPPS/SPDP '99 Workshop on Job Scheduling Strategies for Parallel Processing. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. Tewari,G., and Maes,P., (2000), "A Generalized Platform for the Specification, Valuation, and Brokering of Heterogeneous Resources in Electronic Markets", in Lecture Notes in Artificial Intelligence (LNAI), 2033, Springer-Verlag, pp.7-24. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. Web Quality of Service (WebQoS), (2001), Hewlett-Packard, Technical White Paper, http://www.hp.com/products1/webqos/infolibrary/whitepapers/wp.htmlGoogle ScholarGoogle Scholar
  14. Wolter, K., and Moorsel,A., (2001), "The Relationship between Quality of Service and Business Metrics: Monitoring, Notification and Optimization", Hewlett-Packard Labs Technical Report HPL-2001-96.Google ScholarGoogle Scholar

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              cover image ACM Conferences
              SAC '02: Proceedings of the 2002 ACM symposium on Applied computing
              March 2002
              1200 pages
              ISBN:1581134452
              DOI:10.1145/508791

              Copyright © 2002 ACM

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              Publication History

              • Published: 11 March 2002

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