Towards a Pervasive Data Mining Engine—Architecture Overview

  • Rui Peixoto
  • Filipe PortelaEmail author
  • Manuel F. Santos
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 445)


Current data mining engines are difficult to use, requiring optimizations by data mining experts in order to provide optimal results. To solve this problem a new concept was devised, by maintaining the functionality of current data mining tools and adding pervasive characteristics such as invisibility and ubiquity which focus on their users, providing better ease of use and usefulness, by providing autonomous and intelligent data mining processes. This article introduces an architecture to implement a data mining engine, composed by four major components: database; Middleware (control); Middleware (processing); and interface. These components are interlinked but provide independent scaling, allowing for a system that adapts to the user’s needs. A prototype has been developed in order to test the architecture. The results are very promising and showed their functionality and the need for further improvements.


Data mining Pervasive computing Data mining engine 


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  1. 1.
    Weiser, M.: The computer for the 21st century. In. Sci. Am. 265, 94–104 (1991).Google Scholar
  2. 2.
    Weiser, M.: Some computer science issues in ubiquitous computing. Commun. ACM. 36, 75–84 (1993).Google Scholar
  3. 3.
    Lyytinen, K., Yoo, Y.: Issues and challenges in Ubiquitous computing. Commun. ACM. 45, 63–96 (2002).Google Scholar
  4. 4.
    Satyanarayanan, M.: Pervasive computing: Vision and challenges. Pers. Commun. IEEE. 8, 10–17 (2001).Google Scholar
  5. 5.
    Coulouris, G.F., Dollimore, J., Kindberg, T.: Distributed systems: concepts and design. pearson education (2005).Google Scholar
  6. 6.
    Forman, G.H., Zahorjan, J.: The challenges of mobile computing. In. Computer (Long. Beach. Calif). 27, 38–47 (1994).Google Scholar
  7. 7.
    Saha, D., Mukherjee, A.: Pervasive computing: a paradigm for the 21st century. In. Computer (Long. Beach. Calif). 36, 25–31 (2003).Google Scholar
  8. 8.
    Mark, W.: Turning pervasive computing into mediated spaces. IBM Syst. J. 38, 677–692 (1999).Google Scholar
  9. 9.
    Banavar, G., Beck, J., Gluzberg, E., Munson, J., Sussman, J., Zukowski, D.: Challenges: an application model for pervasive computing. In: Proceedings of the 6th annual international conference on Mobile computing and networking. pp. 266–274 (2000).Google Scholar
  10. 10.
    Ye, J., Dobson, S., Nixon, P.: An overview of pervasive computing systems. In: Ambient Intelligence with Microsystems. pp. 3–17. Springer (2008).Google Scholar
  11. 11.
    Fayyad, U.M., Piatetsky-Shapiro, G., Smyth, P., others: Knowledge Discovery and Data Mining: Towards a Unifying Framework. In: KDD. pp. 82–88 (1996).Google Scholar
  12. 12.
    Witten, I.H., Frank, E., Mark, A.: Data Mining: Practical machine learning tools and techniques, (2011).Google Scholar
  13. 13.
    Kantardzic, M.: Data-Mining Concepts. Data Min. Concepts, Model. Methods, Algorithms, Second Ed. 1–25 (2011).Google Scholar
  14. 14.
    Hand, D.J., Mannila, H., Smyth, P.: Principles of data mining. MIT press (2001).Google Scholar
  15. 15.
    Bradley, P.S., Fayyad, U.M., Mangasarian, O.L.: Mathematical programming for data mining: formulations and challenges. In. INFORMS J. Comput. 11, 217–238 (1999).Google Scholar
  16. 16.
    Fayyad, U., Piatetsky-Shapiro, G., Smyth, P.: From data mining to knowledge discovery in databases. AI Mag. 17, 37 (1996).Google Scholar
  17. 17.
    Giraud-Carrier, C., Povel, O.: Characterising data mining software. Intell. Data Anal. 7, 181–192 (2003).Google Scholar
  18. 18.
    Mikut, R., Reischl, M.: Data mining tools. Wiley Interdiscip. Rev. Data Min. Knowl. Discov. 1, 431–443 (2011).Google Scholar
  19. 19.
    Huang, T.C.-K., Liu, C.-C., Chang, D.-C.: An empirical investigation of factors influencing the adoption of data mining tools. Int. J. Inf. Manage. 32, 257–270 (2012).Google Scholar
  20. 20.
    Portela, F., Santos, M.F., Machado, J., Abelha, A., Silva, Á., Rua, F.: Pervasive and intelligent decision support in intensive medicine–the complete picture. In: Information Technology in Bio-and Medical Informatics. pp. 87–102. Springer (2014).Google Scholar
  21. 21.
    Aguiar, J., Portela, F., Santos, M.F., Machado, J., Abelha, A., Silva, Á., Rua, F., Pinto, F.: Pervasive information systems to intensive care medicine: technology aceptance model. In. ICEIS 2013 - 15th International Conference on Enterprise Information Systems. pp 177-184. SciTePress (2013).Google Scholar
  22. 22.
    Portela, F., Gago, P., Santos, M.F., Machado, J., Abelha, A., Silva, Á., Rua, F.: Implementing a Pervasive Real-Time Intelligent System for Tracking Critical Events with Intensive Care Patients. In: Int. J. Healthc. Inf. Syst. Informatics. 8, 1–16 (2013).Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Rui Peixoto
    • 1
  • Filipe Portela
    • 1
    • 2
    Email author
  • Manuel F. Santos
    • 1
  1. 1.Algoritmi Research CentreUniversity of MinhoBragaPortugal
  2. 2.ESEIGPorto PolytechnicPortoPortugal

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