Managing uncertainty in spatial and spatio-temporal data

Reynold Cheng, Tobias Emrich, Hans Peter Kriegel, Nikos Mamoulis, Matthias Renz, Goce P Trajcevski, Andreas Züfle

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

13 Scopus citations

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.

Original languageEnglish (US)
Title of host publication2014 IEEE 30th International Conference on Data Engineering, ICDE 2014
PublisherIEEE Computer Society
Pages1302-1305
Number of pages4
ISBN (Print)9781479925544
DOIs
StatePublished - Jan 1 2014
Event30th IEEE International Conference on Data Engineering, ICDE 2014 - Chicago, IL, United States
Duration: Mar 31 2014Apr 4 2014

Other

Other30th IEEE International Conference on Data Engineering, ICDE 2014
CountryUnited States
CityChicago, IL
Period3/31/144/4/14

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Information Systems

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