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Wisdom-Aware Computing: On the Interactive Recommendation of Composition Knowledge

  • Soudip Roy Chowdhury
  • Carlos Rodríguez
  • Florian Daniel
  • Fabio Casati
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6568)

Abstract

We propose to enable and facilitate the development of service-based development by exploiting community composition knowledge, i.e., knowledge that can be harvested from existing, successful mashups or service compositions defined by other and possibly more skilled developers (the community or crowd) in a same domain. Such knowledge can be used to assist less skilled developers in defining a composition they need, allowing them to go beyond their individual capabilities. The assistance comes in the form of interactive advice, as we aim at supporting developers while they are defining their composition logic, and it adjusts to the skill level of the developer. In this paper we specifically focus on the case of process-oriented, mashup-like applications, yet the proposed concepts and approach can be generalized and also applied to generic algorithms and procedures.

Keywords

Association Rule Service Composition Frequent Itemset Mining Sequential Pattern Mining Modeling Canvas 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Geffner, H.: Perspectives on artificial intelligence planning. In: AAAI 2002, pp. 1013–1023 (2002)Google Scholar
  2. 2.
    Roman, D., de Bruijn, J., Mocan, A., Lausen, H., Domingue, J., Bussler, C., Fensel, D.: WWW: WSMO, WSML, and WSMX in a nutshell. In: Mizoguchi, R., Shi, Z.-Z., Giunchiglia, F. (eds.) ASWC 2006. LNCS, vol. 4185, pp. 516–522. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  3. 3.
    Koschmider, A., Song, M., Reijers, H.A.: Social Software for Modeling Business Processes. In: Ardagna, D., Mecella, M., Yang, J. (eds.) BPM 2008 Workshops. LNBIP, vol. 17, pp. 666–677. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  4. 4.
    Reichling, T., Veith, M., Wulf, V.: Expert Recommender: Designing for a Network Organization. Computer Supported Cooperative Work 16(4-5), 431–465 (2007)CrossRefGoogle Scholar
  5. 5.
    Daniel, F., Casati, F., Benatallah, B., Shan, M.-C.: Hosted Universal Composition: Models, Languages and Infrastructure in mashArt. In: Laender, A.H.F., Castano, S., Dayal, U., Casati, F., de Oliveira, J.P.M. (eds.) ER 2009. LNCS, vol. 5829, pp. 428–443. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  6. 6.
    Greenshpan, O., Milo, T., Polyzotis, N.: Autocompletion for mashups. In: VLDB 2009, pp. 538–549 (2009)Google Scholar
  7. 7.
    Hornung, T., Koschmider, A., Lausen, G.: Recommendation based process modeling support: Method and user experience. In: Li, Q., Spaccapietra, S., Yu, E., Olivé, A. (eds.) ER 2008. LNCS, vol. 5231, pp. 265–278. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  8. 8.
    Riabov, A.V., Bouillet, E., Feblowitz, M.D., Liu, Z., Ranganathan, A.: Wishful Search: Interactive Composition of Data Mashups. In: WWW 2008, pp. 775–784 (2008)Google Scholar
  9. 9.
    Ngu, A.H.H., Carlson, M.P., Sheng, Q.Z.: Semantic-Based Mashup of Composite Applications. IEEE Transactions on Services Computing 3(1) (January-March 2010)Google Scholar
  10. 10.
    Elmeleegy, H., Ivan, A., Akkiraju, R., Goodwin, R.: MashupAdvisor: A Recommendation Tool for Mashup Development. In: ICWS 2008, pp. 337–344 (2008)Google Scholar
  11. 11.
    OMG. Business Process Model and Notation (BPMN) - Version 1.2 (January 2009), http://www.omg.org/spec/BPMN/1.2
  12. 12.
    OASIS. Web Services Business Process Execution Language Version 2.0 (April 2007), http://docs.oasis-open.org/wsbpel/2.0/OS/wsbpel-v2.0-OS.html
  13. 13.
    Smirnov, S., Weidlich, M., Mendling, J., Weske, M.: Object-Sensitive Action Patterns in Process Model Repositories. In: BPM 2010 Workshops, NJ, USA (September 2010)Google Scholar
  14. 14.
    Wegener, D., Rueping, S.: On Reusing Data Mining in Business Processes – A Pattern-based Approach. In: BPM 2010 Workshops, NJ, USA (September 2010)Google Scholar
  15. 15.
    Shearer, C.: The CRISP-DM model: the new blueprint for data mining. Journal of Data Warehousing 5(4), 13–22 (2000)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Soudip Roy Chowdhury
    • 1
  • Carlos Rodríguez
    • 1
  • Florian Daniel
    • 1
  • Fabio Casati
    • 1
  1. 1.University of TrentoPovoItaly

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