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Efficient mobility pattern stream matching on mobile devices

: Florescu, Simona-Claudia; Mock, Michael; Körner, Christine; May, Michael

Volltext urn:nbn:de:0011-n-2212262 (759 KByte PDF)
MD5 Fingerprint: 913a8cf5661605c1e4ac3f2b0fe9ef38
Erstellt am: 6.12.2012

Gama, J.:
2nd Workshop on Ubiquitous Data Mining, UDM 2012 : In conjunction with the 20th European Conference on Artificial Intelligence (ECAI 2012), Montpellier, France; August 27 - 31, 2012
Montpellier, 2012
Workshop on Ubiquitous Data Mining (UDM) <2, 2012, Montpellier>
European Conference on Artificial Intelligence (ECAI) <20, 2012, Montpellier>
Konferenzbeitrag, Elektronische Publikation
Fraunhofer IAIS ()
mobility pattern maching; location base services; spatiotemporal events; GPS; pattern language

The increasing amount of mobile phones that are equipped with localization technology offers a great opportunity for the collection of mobility data. This data can be used for detecting mobility patterns. Matching mobility patterns in streams of spatiotemporal events implies a trade-off between efficiency and pattern complexity. Existing work deals either with low expressive patterns, which can be evaluated efficiently, or with very complex patterns on powerful machines. We propose an approach which solves the trade-off and is able to match flexible and sufficiently complex patterns while delivering a good performance on a resource-constrained mobile device. The supported patterns include full regular expressions as well as relative and absolute time constraints. We present the definition of our pattern language and the implementation and performance evaluation of the pattern matching on a mobile device, using a hierarchy of filters which continuously process the GPS input stream.