Recognition of Periodic Behavioral Patterns from Streaming Mobility Data

Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 131)


Ubiquitous location-aware sensing devices have facilitated collection of large volumes of mobility data streams from moving entities such as people and animals, among others. Extraction of various types of periodic behavioral patterns hidden in such large volume of mobility data helps in understanding the dynamics of activities, interactions, and life style of these moving entities. The ever-increasing growth in the volume and dimensionality of such Big Data on the one hand, and the resource constraints of the sensing devices on the other hand, have made not only high pattern recognition accuracy but also low complexity, low resource consumption, and real-timeness important requirements for recognition of patterns from mobility data. In this paper, we propose a method for extracting periodic behavioral patterns from streaming mobility data which fulfills all these requirements. Our experimental results on both synthetic and real data sets confirm superiority of our method compared with existing techniques.


Memory Requirement Pattern Mining Synthetic Dataset Periodic Pattern Mobility Data 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2014

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity of TwenteEnschedeThe Netherlands

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