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Sinks, Stationary Regions and Thoroughfares in Object Mobility Databases (2005)
BibTeX
@MISC{Verhein05sinks,stationary,
author = {Florian Verhein and Sanjay Chawla and Florian Verhein and Sanjay Chawla},
title = {Sinks, Stationary Regions and Thoroughfares in Object Mobility Databases},
year = {2005}
}
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Abstract
Abstract. As mobile devices proliferate and networks become more locationaware, the corresponding growth in spatio-temporal data will demand analysis techniques to mine patterns that take into account the semantics of such data. Association Rule Mining (ARM) has been one of the more extensively studied data mining techniques, but it considers discrete transactional data (supermarket or sequential). Most attempts to apply this technique to spatial-temporal domains maps the data to transactions, thus losing the spatio-temporal characteristics. We provide a comprehensive definition of spatio-temporal association rules (STARs) that describe how objects move between regions over time. We define support in the spatio-temporal domain to effectively deal with the semantics of such data. We also introduce other patterns that are useful for mobility data; stationary regions and high traffic regions. The latter consists of sources, sinks and thoroughfares. These patterns describe important temporal characteristics of regions and we show that they can be considered as special STARs. We provide efficient algorithms to find these patterns by exploiting several pruning properties. 1
Keyphrases
stationary region object mobility database mobile device proliferate corresponding growth data mining technique spatio-temporal domain spatial-temporal domain association rule mining discrete transactional data special star spatio-temporal association rule spatio-temporal characteristic latter consists mobility data analysis technique high traffic region comprehensive definition spatio-temporal data efficient algorithm important temporal characteristic
