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An Approach for Automatic Discovery, Composition and Invocation of Semantics Web Services for Data Mining

  • Társis MarinhoEmail author
  • Michel Miranda
  • Heitor Barros
  • Evandro Costa
  • Patrick Brito
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10632)

Abstract

Nowadays, several educational institutions make use of e-Learning environments and other technologies to support the teaching and learning process. As a consequence, a large amount of data is generated from the many interactions of students, tutors, teachers and other actors involved in these environments. These data can be a great and important source of information, however, analyzing them is a complex and expensive task. One way to analyze such data properly is to apply Educational Data Mining (EDM) techniques, and thus to use the information obtained in decision making support. There are, however, several challenges in the application of mining in educational data. In particular, the integration challenge is complex because it involves different tools developed in different programming languages. Thus, we propose an approach for automatic discovery, composition and invocation of Semantic Web Services (SWS) for data mining based on a new semantic model. With this, we hope to contribute to a greater flexibility in the integration between data mining tools and e-Learning environments. In order to evaluate, we adopted a scenario-based method to evaluate quality attributes of performance and reliability of the proposed solution in these scenarios.

Keywords

Semantic web services Data mining Educational data mining 

Notes

Acknowledgments

Authors would like to thank Fundação de Amparo à Pesquisa do Estado de Alagoas (FAPEAL) for financial support.

References

  1. 1.
    Barros, H.J.S.: Um middleware adaptável para descoberta, composição e invocação automática de serviços web semântico. Master’s thesis, Federal University of Alagoas (2011)Google Scholar
  2. 2.
    Barros, H.J.S.: Um Modelo Semântico para Compartilhamento de Recursos Educacionais. Ph.D. thesis, Federal University of Campina Grande (2016)Google Scholar
  3. 3.
    IBM. Standards and web services (2017). Accessed 6 Jan 2017Google Scholar
  4. 4.
    IEDMS. What is EDM? (2016). Accessed 10 Oct 2016Google Scholar
  5. 5.
    Luna, J.M., Castro, C., Romero, C.: MDM tool: a data mining framework integrated into moodle. Comput. Appl. Eng. Educ. 25(1), 90–102 (2017)CrossRefGoogle Scholar
  6. 6.
    Martin, D., et al.: OWL-S: semantic markup for web services. W3C Member Submission 22, 2007–04 (2004)Google Scholar
  7. 7.
    MongoDB. What is MongoDB (2017). Accessed 6 Jan 2017Google Scholar
  8. 8.
    Payne, T., Lassila, O.: Guest editors’ introduction: semantic web services. IEEE Intell. Syst. 19(4), 14–15 (2004)CrossRefGoogle Scholar
  9. 9.
    Podpečan, V., Zemenova, M., Lavrač, N.: Orange4WS environment for service-oriented data mining. Comput. J. 55, 82–98 (2011).  https://doi.org/10.1093/comjnl/bxr077CrossRefGoogle Scholar
  10. 10.
    Romero, C., Ventura, S.: Educational data mining: a survey from 1995 to 2005. Expert Syst. Appl. 33(1), 135–146 (2007)CrossRefGoogle Scholar
  11. 11.
    Romero, C., Ventura, S.: Educational data mining: a review of the state of the art. IEEE Trans. Syst. Man Cybern. Part C: Appl. Rev. 40(6), 601–618 (2010)CrossRefGoogle Scholar
  12. 12.
    Romero, C., Ventura, S.: Data mining in education. Wiley Interdiscip. Rev.: Data Min. Knowl. Discov. 3(1), 12–27 (2013)Google Scholar
  13. 13.
    Schwarz, M., Lobur, M., Stekh, Y.: Analysis of the effectiveness of similarity measures for recommender systems. In: 2017 14th International Conference on Experience of Designing and Application of CAD Systems in Microelectronics (CADSM), pp. 275–277. IEEE (2017)Google Scholar
  14. 14.
    Souza, T.M.: Um framework para mineração de dados educacionais basedo em serviçõs semânticos. Master’s thesis, Federal University of Alagoas (2011)Google Scholar
  15. 15.
    Zorrilla, M., García-Saiz, D.: A service oriented architecture to provide data mining services for non-expert data miners. Decis. Support. Syst. 55(1), 399–411 (2013)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Társis Marinho
    • 1
    • 2
    Email author
  • Michel Miranda
    • 3
  • Heitor Barros
    • 1
  • Evandro Costa
    • 3
  • Patrick Brito
    • 3
  1. 1.Federal Institute of Alagoas - IFALMaceióBrazil
  2. 2.Federal University of Campina Grande - UFCGCampina GrandeBrazil
  3. 3.Federal Univeristy of Alagoas - UFALMaceióBrazil

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