• DocumentCode
    591847
  • Title

    Visualizing large sensor network data sets in space and time with vizzly

  • Author

    Keller, Matthias ; Beutel, Jan ; Saukh, Olga ; Thiele, Lothar

  • Author_Institution
    Comput. Eng. & Networks Lab., ETH Zurich, Zurich, Switzerland
  • fYear
    2012
  • fDate
    22-25 Oct. 2012
  • Firstpage
    925
  • Lastpage
    933
  • Abstract
    This paper presents Vizzly, a middleware for the interactive browsing of large sensor network data sets. Provided map and line plot widgets allow to visualize structured data from mobile and static sensors. A user is completely free in selecting sensor data based on time and location, suitable levels of temporal and spatial detail are automatically chosen by the Vizzly server. Vizzly automatically adapts to user interactions, new data is automatically loaded when query parameters change. Request response times are significantly reduced by the use of caching techniques, most requests are served from already pre-computed data that is stored in the memory of the Vizzly server. Vizzly has already been successfully integrated into the PermaSense and OpenSense projects, a single instance is currently handling more than 550 millions of data points.
  • Keywords
    data structures; data visualisation; middleware; telecommunication computing; wireless sensor networks; OpenSense projects; PermaSense projects; Vizzly; Wireless sensor networks; caching techniques; data structure visualization; interactive browsing; middleware; mobile sensors; query parameters; spatial detail; static sensors; temporal detail; visualizing large sensor network data sets; Aggregates; Arrays; Data visualization; Mobile communication; Sensors; Spatial resolution; Time series analysis; Big Data; Caching; Mobile Sensing; Visualization; Wireless Sensor Networks;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Local Computer Networks Workshops (LCN Workshops), 2012 IEEE 37th Conference on
  • Conference_Location
    Clearwater, FL
  • Print_ISBN
    978-1-4673-2130-3
  • Type

    conf

  • DOI
    10.1109/LCNW.2012.6424084
  • Filename
    6424084