• DocumentCode
    2002091
  • Title

    Adaptive Interpolation Algorithms for Temporal-Oriented Datasets

  • Author

    Gao, Jun

  • Author_Institution
    Dept. of Comput. Sci. & Eng., Nebraska Univ., Lincoln, NE
  • fYear
    2006
  • fDate
    15-17 June 2006
  • Firstpage
    145
  • Lastpage
    151
  • Abstract
    Spatiotemporal datasets can be classified into two categories: temporal-oriented and spatial-oriented datasets depending on whether missing spatiotemporal values are closer to the values of its temporal or spatial neighbors. We present an adaptive spatiotemporal interpolation model that can estimate the missing values in both categories of spatiotemporal datasets. The key parameters of the adaptive spatiotemporal interpolation model can be adjusted based on experience
  • Keywords
    interpolation; temporal databases; visual databases; adaptive spatiotemporal interpolation model; spatial-oriented datasets; spatiotemporal datasets; temporal-oriented datasets; Computer science; Data engineering; Data visualization; Interpolation; Nominations and elections; Pollution; Shape; Spatiotemporal phenomena; Temperature; Voting;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Temporal Representation and Reasoning, 2006. TIME 2006. Thirteenth International Symposium on
  • Conference_Location
    Budapest
  • ISSN
    1530-1311
  • Print_ISBN
    0-7695-2617-9
  • Type

    conf

  • DOI
    10.1109/TIME.2006.4
  • Filename
    1635992