Adaptive clutter-aware visualization for mobile data stream mining

M. Gaber, S. Krishnaswamy, B. Gillick, N. Nicoloudis, J. Liono, H. Al-Taiar, A. Zaslavsky

Research output: Chapter in Book/Report/Conference proceedingConference contribution


There is an emerging focus on real-time data stream analysis on mobile devices. A wide range of data stream processing applications are targeted to run on mobile handheld devices with limited computational capabilities such as patient monitoring, driver monitoring, providing real-time analysis and visualization for emergency and disaster management, real-time optimization for courier pick-up and delivery etc. There are many challenges in visualization of the analysis/data stream mining results on a mobile device. These include coping with the small screen real-estate and effective presentation of highly dynamic and real-time analysis. This paper proposes a generic theory for visualization on small screens that we term Adaptive Clutter Reduction ACR. Based on ACR, we have developed and experimentally validated a novel data stream clustering result visualization technique that we term Clutter-Aware Clustering Visualizer (CACV). Experimental results on both synthetic and real datasets using the Google Andriod platform are presented proving the effectiveness of the proposed techniques.
Original languageEnglish
Title of host publication2010 22nd international conference on tools with artificial intelligence (ICTAI). Vol. 2
Place of PublicationPiscataway
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages304 -311
ISBN (Print)9781424488179
Publication statusPublished - 2010
EventIEEE 22nd International Conference on Tools with Artificial Intelligence - Arras, France
Duration: 27 Oct 201029 Oct 2010


ConferenceIEEE 22nd International Conference on Tools with Artificial Intelligence
Abbreviated titleICTAI 2010


  • adaptive clutter aware visualization
  • computational capabilities
  • data stream processing applications
  • disaster management
  • mobile data stream mining
  • mobile devices
  • mobile handheld devices
  • real-time analysis
  • data mining
  • data visualisation
  • mobile computing
  • real-time systems


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