Managing uncertain spatio-temporal data

Authors:
Thomas Bernecker;Tobias Emrich;Hans-Peter Kriegel;Andreas Zuefle;Lei Chen;Xiang Lian;Nikos Mamoulis
Affiliations:
Ludwig-Maximilians-Universität München, Germany;Ludwig-Maximilians-Universität München, Germany;Ludwig-Maximilians-Universität München, Germany;Ludwig-Maximilians-Universität München, Germany;Hong Kong University of Science and Technology, Kowloon, Hong Kong;Hong Kong University of Science and Technology, Kowloon, Hong Kong;University of Hong Kong, Hong Kong
Venue:
Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Querying and Mining Uncertain Spatio-Temporal Data
Year:
2011

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Abstract

Many spatial query problems defined on uncertain data are computationally expensive, in particular, if in addition to spatial attributes, a time component is added. Although there exists a wide range of applications dealing with uncertain spatio-temporal data, there is no solution for efficient management of such data available yet. This paper is the first work to propose general models for spatio-temporal uncertain data that have the potential to allow efficient processing on a wide range of queries. The main challenge here is to unfold this potential by developing new algorithms based on these models. In addition, we give examples of interesting spatio-temporal queries on uncertain data.