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
Link To Document :
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