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.
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Camossi, E., Bertolotto, M., Kechadi, T. (2008). Mining Spatio-Temporal Data at Different Levels of Detail. In: Bernard, L., Friis-Christensen, A., Pundt, H. (eds) The European Information Society. Lecture Notes in Geoinformation and Cartography. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78946-8_12
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DOI: https://doi.org/10.1007/978-3-540-78946-8_12
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