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Mining Spatio-temporal Data at Different Levels of Detail

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Download CamossiAGILE2008.pdf2.45 MB
Author(s)
Camossi, Elena 
Bertolotto, Michela 
Kechadi, Tahar 
Uri
http://hdl.handle.net/10197/1442
Date Issued
08 May 2008
Date Available
28T14:22:56Z September 2009
Abstract
In this paper we propose a methodology for mining very large spatio-temporal datasets. We propose a two-pass strategy for mining and manipulating spatio-temporal datasets at different levels of detail (i.e., granularities). The approach takes advantage of the multi-granular capability of the underlying spatio-temporal model to reduce the amount of data that can be accessed initially. The approach is implemented and applied to real-world spatio-temporal datasets. We show that the technique can deal easily with very large datasets without losing the accuracy of the extracted patterns, as demonstrated in the experimental results.
Sponsorship
Science Foundation Ireland; Irish Research Council for Science, Engineering & Technology
Type of Material
Conference Publication
Publisher
Springer-Verlag
Copyright (Published Version)
Springer 2008
Keywords
  • Spatio-temporal data ...

  • Spatio-temporal multi...

Subject – LCSH
Data mining
Granular computing
DOI
10.1007/978-3-540-78946-8_12
Web versions
http://dx.doi.org/10.1007/978-3-540-78946-8_12
Language
English
Status of Item
Peer reviewed
Part of
Bernard, L., Friis-Christen, A., and Pundt, H. (eds.) The European Information Society : taking geo-information science one step further
Description
Presented at the 11th AGILE International Conference on Geographic Information Science (AGILE 2008), Girona, Spain, 5-8 May 2008
ISBN
978-3-540-78945-1
This item is made available under a Creative Commons License
https://creativecommons.org/licenses/by-nc-sa/1.0/
Owning collection
Computer Science Research Collection
Scopus© citations
6
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Feb 4, 2023
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