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Situation-Aware Adaptive Visualization for Sensory Data Stream Mining

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Knowledge Discovery from Sensor Data (Sensor-KDD 2008)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 5840))

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Abstract

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.

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Authors and Affiliations

  1. Centre for Distributed Systems and Software Engineering, Monash University, Caulfield Campus, 900 Dandenong Rd, Caulfield East, VIC, 3145, Australia

    Pari Delir Haghighi, Brett Gillick, Shonali Krishnaswamy, Mohamed Medhat Gaber & Arkady Zaslavsky

Authors
  1. Pari Delir Haghighi
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  2. Brett Gillick
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  3. Shonali Krishnaswamy
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  4. Mohamed Medhat Gaber
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  5. Arkady Zaslavsky
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Editor information

Editors and Affiliations

  1. Centre for Distributed Systems and Software Engineering, Monash University, 900 Dandenong Road, Caulfield East, 3145, Melbourne, VIC, Australia

    Mohamed Medhat Gaber

  2. Oak Ridge National Laboratory, Computational Sciences and Engineering Division, 37831, Oak Ridge, TN, USA

    Ranga Raju Vatsavai , Olufemi A. Omitaomu  & Auroop R. Ganguly ,  & 

  3. Faculty of Economics, LIAAD-INESC Porto, University of Porto, L.A. Rua de Ceuta, 118, 6, 4050-190, Porto, Portugal

    João Gama

  4. Computer Science and Engineering Department, University of Notre Dame, 353 Fitzpatrick Hall, 46556, Notre Dame, IN, USA

    Nitesh V. Chawla

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Haghighi, P.D., Gillick, B., Krishnaswamy, S., Gaber, M.M., Zaslavsky, A. (2010). Situation-Aware Adaptive Visualization for Sensory Data Stream Mining. In: Gaber, M.M., Vatsavai, R.R., Omitaomu, O.A., Gama, J., Chawla, N.V., Ganguly, A.R. (eds) Knowledge Discovery from Sensor Data. Sensor-KDD 2008. Lecture Notes in Computer Science, vol 5840. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12519-5_3

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  • DOI: https://doi.org/10.1007/978-3-642-12519-5_3

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-12518-8

  • Online ISBN: 978-3-642-12519-5

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