• DocumentCode
    141006
  • Title

    Managing uncertainty in spatial and spatio-temporal data

  • Author

    Cheng, Russell ; Emrich, T. ; Kriegel, Hans-Peter ; Mamoulis, Nikos ; Renz, M. ; Trajcevski, G. ; Zufle, A.

  • Author_Institution
    Dept. of Comput. Sci., Univ. of Hong Kong, Hong Kong, China
  • fYear
    2014
  • fDate
    March 31 2014-April 4 2014
  • Firstpage
    1302
  • Lastpage
    1305
  • Abstract
    Location-related data has a tremendous impact in many applications of high societal relevance and its growing volume from heterogeneous sources is one true example of a Big Data [1]. An inherent property of any spatio-temporal dataset is uncertainty due to various sources of imprecision. This tutorial provides a comprehensive overview of the different challenges involved in managing uncertain spatial and spatio-temporal data and presents state-of-the-art techniques for addressing them.
  • Keywords
    Big Data; visual databases; Big Data; location-related data; spatial data; spatio-temporal data; uncertainty management; Data models; Probabilistic logic; Semantics; Spatial databases; Tutorials; Uncertainty;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Data Engineering (ICDE), 2014 IEEE 30th International Conference on
  • Conference_Location
    Chicago, IL
  • Type

    conf

  • DOI
    10.1109/ICDE.2014.6816766
  • Filename
    6816766