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Implementing an Open Reference Architecture Based on Web Service Mining for the Integration of Distributed Applications and Multi-Agent Systems

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Agents and Data Mining Interaction (ADMI 2010)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5980))

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Abstract

This paper introduces an open reference architecture that enables seamless integration of distributed applications and agent-based systems. The reference architecture has been designed in order to be holistic, open and standards-abiding. It is based on an ontological framework that operates as a middleware between application developers and service providers on the one side and multi-agent systems on the other one. The proposed architecture enables seamless integration of closed (standalone) applications, provided that these applications export their functionality in the form of public web services. Moreover, we propose a data mining framework that operates on web service data and performs classification of web services and their operations into their semantically described counterparts. This enables seamless integration of applications through the corresponding web service interfaces, based on the ontological framework. On the other hand we show that knowledge derived from web service mining can be used by multi-agent systems in order to provide composite functionalities derived from the distributed web service operations. Moreover, agents are capable to provide personalized services that fulfill user requirements, by taking into account the personal profile, as well as the history of the end user. The paper presents the prototype tools that have been implemented for the realization of the proposed reference architecture.

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References

  1. Zheng, G., Bouguettaya, A.: Service Mining on the Web. IEEE Transactions on Services Computing 2(1), 65–78 (2009)

    Article  Google Scholar 

  2. Asbagh, M.J., Abolhassani, H.: Web service usage mining: mining for executable sequences. In: Revetria, R., Cecchi, A., Schenone, M., Mladenov, V.M., Zemliak, A. (eds.) Proceedings of the 7th Conference on 7th WSEAS International Conference on Applied Computer Science, vol. 7, pp. 266–271. World Scientific and Engineering Academy and Society (WSEAS), Stevens Point (2007)

    Google Scholar 

  3. MuthuMeena, M., Jayakumar, S.K.V., Dhavachelvan, P.: Service Mining Architecture for Web-based Services using Agents. International Journal of Computer Science And Applications 1(1), 56–59 (2008)

    Google Scholar 

  4. Patil, A.A., Oundhakar, A., Sheth, A.P., Verma, K.: METEOR-S Web service annotation framework. In: Proc. of the 13th International Conference on WWW. ACM Press, New York (2004)

    Google Scholar 

  5. Oldham, N., Thomas, C., Sheth, A., Verma, K.: METEOR-S Web Service Annotation Framework with Machine Learning Classification. In: Cardoso, J., Sheth, A.P. (eds.) SWSWPC 2004. LNCS, vol. 3387, pp. 137–146. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  6. Heß, A., Kushmerick, N.: Learning to attach semantic metadata to Web services. In: Fensel, D., Sycara, K., Mylopoulos, J. (eds.) ISWC 2003. LNCS, vol. 2870, pp. 258–273. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

  7. Corella, M.A., Castells, P.: Semi-automatic semantic-based Web service classification. In: Eder, J., Dustdar, S. (eds.) BPM Workshops 2006. LNCS, vol. 4103, pp. 459–470. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  8. Crasso, M., Zunino, A., Campo, M.: Awsc: An approach to Web service classification based on machine learning techniques. Inteligencia Artificial, Revista Iberoamericana de IA 12(37), 25–36 (2008)

    Google Scholar 

  9. Feldman, R., Sanger, J.: The Text Mining Handbook: Advanced Approaches in Analyzing unstructured Data. Cambridge University Press, Cambridge (2006)

    Book  Google Scholar 

  10. Porter, M.: An algorithm for suffix stripping. Program 14(3), 130–137 (1980)

    Article  Google Scholar 

  11. Lan, M., Tan, C.-L., Low, H.-W., Sung, S.-Y.: A comprehensive comparative study on term weighting schemes for text categorization with support vector machines. In: Special Interest Tracks and Posters of 14th International Conference on WWW (2005)

    Google Scholar 

  12. Pedersen, T., Patwardhan, S., Michelizzi, J.: Word Net: Similarity - Measuring the Relatedness of Concepts. In: Proceedings of Fifth Annual Meeting of the North American Chapter of the Association for Computational Linguistics (NAACL-04), Boston, MA, May 3-5, pp. 38–41 (2004)

    Google Scholar 

  13. Burkard, R.E., Dell’Amico, M., Martello, S.: Assignment Problems. SIAM, Philadelphia (2009)

    Book  MATH  Google Scholar 

  14. Wooldridge, M.: Reasoning about rational agents. MIT Press, Cambridge (2009)

    MATH  Google Scholar 

  15. Kohavi, R.: A study of cross-validation and bootstrap for accuracy estimation and model selection. In: Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence, vol. 2(12), pp. 1137–1143 (1995)

    Google Scholar 

  16. John, G.H., Langley, P.: Estimating Continuous Distributions in Bayesian Classifiers. In: Eleventh Conference on Uncertainty in Artificial Intelligence, pp. 338–345. Morgan Kaufmann Publishers, San Mateo (1995)

    Google Scholar 

  17. Keerthi, S.S., Shevade, S.K., Bhattacharyya, C., Murthy, K.R.K.: Improvements to Platt’s SMO Algorithm for SVM Classifier Design. Neural Computation 13(3), 637–649 (2001)

    Article  MATH  Google Scholar 

  18. McCallum, A., Nigam, K.: A Comparison of Event Models for Naive Bayes Text Classification. In: AAAI-98 Workshop on Learning for Text Categorization (1998)

    Google Scholar 

  19. Su, J., Zhang, H., Ling, C.X., Matwin, S.: Discriminative Parameter Learning for Bayesian Networks. In: International Conference on Machine Learning, ICML (2008)

    Google Scholar 

  20. Frank, E., Witten, I.H.: Generating Accurate Rule Sets Without Global Optimization. In: Fifteenth International Conference on Machine Learning, pp. 144–151 (1998)

    Google Scholar 

  21. Pautasso, C., Zimmermann, O., Leymann, F.: Restful web services vs. ”big’” web services: making the right architectural decision. In: Proceeding of the 17th international Conference on World Wide Web, WWW ’08, Beijing, China, April 21-25, pp. 805–814. ACM, New York (2008)

    Chapter  Google Scholar 

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Kehagias, D.D., Tzovaras, D., Mavridou, E., Kalogirou, K., Becker, M. (2010). Implementing an Open Reference Architecture Based on Web Service Mining for the Integration of Distributed Applications and Multi-Agent Systems. In: Cao, L., Bazzan, A.L.C., Gorodetsky, V., Mitkas, P.A., Weiss, G., Yu, P.S. (eds) Agents and Data Mining Interaction. ADMI 2010. Lecture Notes in Computer Science(), vol 5980. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15420-1_14

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  • DOI: https://doi.org/10.1007/978-3-642-15420-1_14

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-15419-5

  • Online ISBN: 978-3-642-15420-1

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