Situation-Aware Adaptive Visualization for Sensory Data Stream Mining

  • Pari Delir Haghighi
  • Brett Gillick
  • Shonali Krishnaswamy
  • Mohamed Medhat Gaber
  • Arkady Zaslavsky
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5840)


With the emergence of ubiquitous data mining and recent advances in mobile communications, there is a need for visualization techniques to enhance the user-interactions, real-time decision making and comprehension of the results of mining algorithms. In this paper we propose a novel architecture for situation-aware adaptive visualization that applies intelligent visualization techniques to data stream mining of sensory data. The proposed architecture incorporates fuzzy logic principles for modeling and reasoning about context/situations and performs gradual adaptation of data mining and visualization parameters according to the occurring situations. A prototype of the architecture is implemented and described in the paper through a real-world scenario in the area of healthcare monitoring.


Data Stream Mining Fuzzy logic Context-aware Visualization 


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  1. 1.
    Aggarwal, C.C.: A Framework for Diagnosing Changes in Evolving Data Streams. In: Proceedings of the ACM SIGMOD Conference (2003)Google Scholar
  2. 2.
    Alive Technologies,
  3. 3.
    Anagnostopoulos, C.B., Ntarladimas, Y., Hadjiefthymiades, S.: Situational Computing: An Innovative Architecture with Imprecise Reasoning. The Journal of Systems and Software 80, 1993–2014 (2007)CrossRefGoogle Scholar
  4. 4.
    Burkhard, R.: Learning from Architects, The Difference between Knowledge Visualization and Information Visualization. In: Eight International Conference on Information Visualization (IV 2004), London, pp. 519–524 (2004)Google Scholar
  5. 5.
    Byun, H., Keith, C.: Supporting Proactive ‘Intelligent’ Behaviour: the Problem of Uncertainty. In: Proceedings of the UM 2003 Workshop on User Modeling for Ubiquitous Computing, Johnstown, PA, pp. 17–25 (2003)Google Scholar
  6. 6.
    Cao, J., Xing, N., Chan, A., Feng, Y., Jin, B.: Service Adaptation Using Fuzzy Theory in Context-aware Mobile Computing Middleware. In: Proceedings of the 11th IEEE Conference on Embedded and Real-time Computing Systems and Applications, RTCSA 2005 (2005)Google Scholar
  7. 7.
    Chen, Y., Leong, H., Xu, H., Cao, M., Chan, J., Chan, K.: In-network Data Processing for Wireless Sensor Networks. In: Proceedings of the 7th International Conference on Mobile Data Management, MDM 2006 (2006)Google Scholar
  8. 8.
    Cheung, R.: An Adaptive Middleware Infrastructure Incorporating Fuzzy Logic for Mobile computing. In: Proceedings of the International Conference on Next Generation Web Services Practices, NWeSP 2005 (2005)Google Scholar
  9. 9.
    de Oliveira, M.C.F., Levkowitz, H.: From visual data exploration to visual data mining: A survey. IEEE Trans. on Visualization and Computer Graphics 9(3), 378–394 (2003)CrossRefGoogle Scholar
  10. 10.
    Gaber, M., Krishnaswamy, S., Zaslavsky, A.: Adaptive Mining Techniques for Data Streams Using Algorithm Output Granularity. In: The Australasian Data Mining Workshop, Held in conjunction with the 2003 Congress on Evolutionary Computation, AusDM 2003, Canberra, Australia. LNCS, Springer, Heidelberg (2003)Google Scholar
  11. 11.
    Gaber, M.M., Krishnaswamy, S., Zaslavsky, A.: Ubiquitous Data Stream Mining. In: Current Research and Future Directions Workshop Proceedings held in conjunction with The Eighth Pacific-Asia Conference on Knowledge Discovery and Data Mining, Sydney, Australia (2004)Google Scholar
  12. 12.
    Gaber, M., Krishnaswamy, S., Zaslavsky, A.: On-board Mining of Data Streams in Sensor Networks. In: Badhyopadhyay, S., Maulik, U., Holder, L., Cook, D. (eds.) Advanced Methods of Knowledge Discovery from Complex Data. Springer, Heidelberg (2005)Google Scholar
  13. 13.
    Gaber, M.M., Yu, P.S.: Detection and Classification of Changes in Evolving Data Streams. International Journal of Information Technology and Decision Making 5(4), 659–670 (2006)CrossRefGoogle Scholar
  14. 14.
    Galan, M., Liu, H., Torkkola, K.: Intelligent Instance Selection of Data Streams for Smart Sensor Applications. In: SPIE Defense and Security Symposium, Intelligent Computing: Theory and Applications III, pp. 108–119 (2005)Google Scholar
  15. 15.
    Gillick, B., Krishnaswamy, S., Gaber, M., Zaslavsky, A.: Visualisation of Fuzzy Classification of Data Elements in Ubiquitous Data Stream Mining. In: IWUC 2006, pp. 29–38 (2006)Google Scholar
  16. 16.
    Horovitz, O., Krishnaswamy, S., Gaber, M.M.: A fuzzy approach for interpretation of ubiquitous data stream clustering and its application in road safety. Intell. Data Anal. 11(1), 89–108 (2007)Google Scholar
  17. 17.
    Hossain, A.: An intelligent sensor network system coupled with statistical process model for predicting machinery health and failure. In: Sensors for Industry Conference (2002)Google Scholar
  18. 18.
    Jang, J., Sun, C., Mizutani, E.: Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence. Prentice-Hall, Upper Saddle River (1997)Google Scholar
  19. 19.
    Kargupta, H., Park, B., Pittie, S., Liu, L., Kushraj, D., Sarkar, K.: MobiMine: Monitoring the Stock Market from a PDA. SIGKDD Explorations 3(2), 37–46 (2002)CrossRefGoogle Scholar
  20. 20.
    Kargupta, H., Bhargava, R., Liu, K., Powers, M., Blair, P., Bushra, S., Dull, J., Sarkar, K., Klein, M., Vasa, M., Handy, D.: VEDAS: A Mobile and Distributed Data Stream Mining System for Real-Time Vehicle Monitoring. In: Proceedings of the SIAM International Data Mining Conference, SDM 2004 (2004)Google Scholar
  21. 21.
    Keim, D.A.: Information visualization and visual data mining. IEEE Transactions On Visualization And Computer Graphics 8(1), 1–8 (2002)CrossRefGoogle Scholar
  22. 22.
    Leijidekkers, P., Gay, V.: Personal Heart Monitoring and Rehabilitation System using Smart Phones. In: Proceedings of the International Conference on Mobile Business, ICMB 2005 (2005)Google Scholar
  23. 23.
    Liu, D., Sprague, A.P., Manne, U.: JRV: an interactive tool for data mining visualization. In: ACM Southeast Regional Conference 2004, pp. 442–447 (2004)Google Scholar
  24. 24.
    Mäntyjärvi, J., Seppanen, T.: Adapting Applications in Mobile Terminals Using Fuzzy Context Information. In: Paternó, F. (ed.) Mobile HCI 2002. LNCS, vol. 2411, pp. 95–107. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  25. 25.
    Padovitz, A., Loke, S., Zaslavsky, A.: Towards a Theory of Context Spaces. In: Proceedings of the 2nd IEEE Annual Conference on Pervasive Computing and Communications, Workshop on Context Modeling and Reasoning CoMoRea 2004. IEEE Computer Society, Orlando (2004)Google Scholar
  26. 26.
    Padovitz, A., Zaslavsky, A., Loke, S.: A Unifying Model for Representing and Reasoning About Context under Uncertainty. In: 11th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems IPMU 2006, Paris, France (2006)Google Scholar
  27. 27.
    Phung, N., Gaber, M., Roehm, U.: Resource-aware Distributed Online Data Mining for Wireless Sensor Networks. In: Proceedings of the International Workshop on Knowledge Discovery from Ubiquitous Data Streams (IWKDUDS 2007), in conjunction with ECML and PKDD 2007, Warsaw, Poland (2007)Google Scholar
  28. 28.
    Ranganathan, A., Al-Muhtadi, J., Campbell, R.: Reasoning about Uncertain Contexts in Pervasive Computing Environments. IEEE Pervasive Computing 3(2), 62–70 (2004)CrossRefGoogle Scholar
  29. 29.
    Rubel, P., Fayn, J., Nollo, G., Assanelli, D., Li, B., Restier, L., Adami, S., Arod, S., Atoui, H., Ohlsson, M., Simon-Chautemps, L., Te´lisson, D., Malossi, C., Ziliani, G., Galassi, A., Edenbrandt, L., Chevalier, P.: Toward Personal eHealth in Cardiology: Results from the EPI-MEDICS Telemedicine Project. Journal of Electrocardiology 38, 100–106 (2005)CrossRefGoogle Scholar
  30. 30.
    Vajirkar, P., Singh, S., Lee, Y.: Context-Aware Data Mining Framework for Wireless Medical Application. In: Mařík, V., Štěpánková, O., Retschitzegger, W. (eds.) DEXA 2003. LNCS, vol. 2736, pp. 381–391. Springer, Heidelberg (2003)Google Scholar
  31. 31.
    Wegman, E., Marchette, D.: On some techniques for streaming data: A case study of Inter-net packet headers. Journal of Computational and Graphical Statistics 12(4), 893–914 (2003)CrossRefMathSciNetGoogle Scholar
  32. 32.
    Zadeh, Z.: The Concept of a Linguistic Variable and Its Application to Approximate Reasoning. Information Systems, 199–249 (1975)Google Scholar
  33. 33.
    Zimmermann, H.: Fuzzy Set Theory - and Its Applications. Kluwer Academic Publishers, Norwell (1996)zbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Pari Delir Haghighi
    • 1
  • Brett Gillick
    • 1
  • Shonali Krishnaswamy
    • 1
  • Mohamed Medhat Gaber
    • 1
  • Arkady Zaslavsky
    • 1
  1. 1.Centre for Distributed Systems and Software EngineeringMonash UniversityCaulfield EastAustralia

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