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Obtaining an Optimal MAS Configuration for Agent-Enhanced Mining Using Constraint Optimization

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Agents and Data Mining Interaction (ADMI 2011)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7103))

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Abstract

We investigate an interaction mechanism between agents and data mining, and focus on agent-enhanced mining. Existing data mining tools use workflow to capture user requirements. The workflow enactment can be improved with a suitable underlying execution layer, which is a Multi-Agent System (MAS). From this perspective, we propose a strategy to obtain an optimal MAS configuration from a given workflow when resource access restrictions and communication cost constraints are concerned, which is essentially a constraint optimization problem. In this paper, we show how workflow is modeled in the way that can be optimized, and how the optimized model is used to obtain an optimal MAS configuration. Finally, we demonstrate that our strategy can improve the load balancing and reduce the communication cost during the workflow enactment.

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References

  1. Bauer, T., Dadam, P.: Efficient Distributed Workflow Management Based on Variable Server Assignments. In: Wangler, B., Bergman, L.D. (eds.) CAiSE 2000. LNCS, vol. 1789, pp. 94–109. Springer, Heidelberg (2000)

    Chapter  Google Scholar 

  2. Buhler, P.A., Vidal, J.M.: Towards Adaptive Workflow Enactment Using Multiagent Systems. Information Technology and Management 6(1), 61–87 (2005)

    Article  Google Scholar 

  3. Cao, L., Gorodetsky, V., Mitkas, P.: Agent mining: The synergy of agents and data mining. IEEE Intelligent Systems 24(3), 64–72 (2009)

    Article  Google Scholar 

  4. Cox, J., Durfee, E.: An efficient algorithm for multiagent plan coordination. In: Proceedings of the Fourth International Joint Conference on Autonomous Agents and Multiagent Systems, pp. 828–835. ACM (2005)

    Google Scholar 

  5. Ehrler, L., Fleurke, M., Purvis, M., Savarimuthu, B.: Agent-based workflow management systems (WfMSs). Information Systems and E-Business Management 4(1), 5–23 (2006)

    Article  Google Scholar 

  6. Huhns, M.N.: Agents as Web services. IEEE Internet Computing 6(4), 93–95 (2002)

    Article  Google Scholar 

  7. Judge, D.W., Odgers, B.R., Shepherdson, J.W., Cui, Z.: Agent-enhanced Workflow. BT Technology Journal 16(3), 79–85 (1998)

    Article  Google Scholar 

  8. Klusch, M., Lodi, S., Gianluca, M.: The role of agents in distributed data mining: issues and benefits. IEEE Comput. Soc. (2003)

    Google Scholar 

  9. Moemeng, C., Zhu, X., Cao, L.: Integrating Workflow into Agent-Based Distributed Data Mining Systems. In: Cao, L., Bazzan, A.L.C., Gorodetsky, V., Mitkas, P.A., Weiss, G., Yu, P.S. (eds.) ADMI 2010. LNCS, vol. 5980, pp. 4–15. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  10. Moemeng, C., Zhu, X., Cao, L., Jiahang, C.: i-Analyst: An Agent-Based Distributed Data Mining Platform. IEEE (December 2010)

    Google Scholar 

  11. Odgers, B.R., Shepherdson, J.W., Thompson, S.G.: Distributed Workflow Co-ordination by Proactive Software Agents. In: Intelligent Workflow and Process Management. The New Frontier for AI in Business IJCAI 1999 Workshop (1999)

    Google Scholar 

  12. Savarimuthu, B.T., Purvis, M., Purvis, M., Cranefield, S.: Agent-based integration of Web Services with Workflow Management Systems. In: AAMAS 2005: Proceedings of the Fourth International Joint Conference on Autonomous Agents and Multiagent Systems, pp. 1345–1346. ACM, New York (2005)

    Chapter  Google Scholar 

  13. Weld, D.S.: An Introduction to Least Commitment Planning. AI Magazine 15(4), 27–61 (1994)

    Google Scholar 

  14. Yoo, J.-J., Suh, Y.-H., Lee, D.-I., Jung, S.-W., Jang, C.-S., Kim, J.-B.: Casting Mobile Agents to Workflow Systems: On Performance and Scalability Issues. In: Mayr, H.C., Lazanský, J., Quirchmayr, G., Vogel, P. (eds.) DEXA 2001. LNCS, vol. 2113, pp. 254–263. Springer, Heidelberg (2001)

    Chapter  Google Scholar 

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Author information

Authors and Affiliations

  1. Quantum Computing and Intelligent Systems, Faulty of Engineering and Information Technology, University of Technology, Sydney, P.O. Box 123, Broadway, NSW, 2007, Australia

    Chayapol Moemeng, Can Wang & Longbing Cao

Authors
  1. Chayapol Moemeng
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  2. Can Wang
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  3. Longbing Cao
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Editor information

Editors and Affiliations

  1. Faculty of Engineering and Information Technology, University of Technology Sydney, Broadway, PO Box 123, 2007, Sydney, NSW, Australia

    Longbing Cao

  2. Instituto de Informática, Universidade Federal do Rio Grande do Sul (UFRGS), Caixa Postal 15064, 91.501-970, Porto Alegre, RS, Brazil

    Ana L. C. Bazzan

  3. Electrical and Computer Engineering Department, Aristotle University of Thessaloniki, 54124, Thessaloniki, Greece

    Andreas L. Symeonidis

  4. St. Petersburg Institute for Informatics and Automation of the Russian Academy of Sciences, 39, 14th Liniya, 199178, St. Petersburg, Russia

    Vladimir I. Gorodetsky

  5. Department of Knowledge Engineering, Maastricht University, P.O. Box 616, 6200, Maastricht, MD, The Netherlands

    Gerhard Weiss

  6. Department of Computer Science, University of Illinois at Chicago, 851 S. Morgan Street, Room 1138 SEO, 60607, Chicago, IL, USA

    Philip S. Yu

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© 2012 Springer-Verlag Berlin Heidelberg

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Moemeng, C., Wang, C., Cao, L. (2012). Obtaining an Optimal MAS Configuration for Agent-Enhanced Mining Using Constraint Optimization. In: Cao, L., Bazzan, A.L.C., Symeonidis, A.L., Gorodetsky, V.I., Weiss, G., Yu, P.S. (eds) Agents and Data Mining Interaction. ADMI 2011. Lecture Notes in Computer Science(), vol 7103. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27609-5_5

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  • DOI: https://doi.org/10.1007/978-3-642-27609-5_5

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-27608-8

  • Online ISBN: 978-3-642-27609-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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