Abstract
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.
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Notes
- 1.
Fourier transfrom is also used for period detection. However, this method has a low performance in identifying large periods [15].
References
Baratchi, M., Meratnia, N., Havinga, P.J.M.: On the use of mobility data for discovery and description of social ties. In: Proceedings of IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2013), Niagara Falls, Canada (2013)
Wisdom, M.J., et al.: Spatial partitioning by mule deer and elk in relation to traffic. In: Transactions of the 69th North American Wildlife and Natural Resources Conference, pp. 509–530 (2004)
Baratchi, M., et al.: Sensing solutions for collecting spatio-temporal data for wildlife monitoring applications: a review. Sensors 13, 6054–6088 (2013)
Monroe, S.: Major and minor life events as predictors of psychological distress: Further issues and findings. J. Behav. Med. 6, 189–205 (1983). 1983/06/01
Aflaki, S., et al.: Evaluation of incentives for body area network-based HealthCare systems. In: Proceedings of IEEE ISSNIP, Melbourne, Australia, (2013)
Agrawal, R., Imielinski, T., Swami, A.: Mining association rules between sets of items in large databases. In: Proceedings of 1993 ACM SIGMOD International Conference on Management of Data, Washington, D.C., USA (1993)
Verhein, Florian, Chawla, Sanjay: Mining spatio-temporal association rules, sources, sinks, stationary regions and thoroughfares in object mobility databases. In: Li Lee, Mong, Tan, Kian-Lee, Wuwongse, Vilas (eds.) DASFAA 2006. LNCS, vol. 3882, pp. 187–201. Springer, Heidelberg (2006)
Giannotti, F., et al.: Trajectory pattern mining. In: Proceedings of 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Jose, California, USA (2007)
Wei, L.-Y., Zheng, Y., Peng, W.-C.: Constructing popular routes from uncertain trajectories. In: Proceedings of 18th ACM SIGKDD, Beijing, China (2012)
Mamoulis, N., et al.: Mining, indexing, and querying historical spatiotemporal data. In: Proceedings of tenth ACM SIGKDD, Seattle, WA, USA (2004)
Baratchi, M., Meratnia, N., Havinga, P.J.M.: Finding frequently visited paths: dealing with the uncertainty of spatio-temporal mobility data. In: Proceedings of IEEE ISSNIP, Melbourne, Australia (2013)
Elfeky, M.G., Aref, W.G., Elmagarmid, A.K.: Periodicity detection in time series databases. IEEE Trans. Knowl. Data Eng. 17, 875–887 (2005)
Jiong, Y., Wei, W., Yu, P.S.: Mining asynchronous periodic patterns in time series data. IEEE Trans. Knowl. Data Eng. 15, 613–628 (2003)
Yang, R., Wang, W., Yu, P.S.: InfoMiner + : mining partial periodic patterns with gap penalties. In: Proceedings of ICDM 2002, pp. 725–728 (2002)
Li, Z., Ding, B., Han, J., Kays, R., Nye, P.: Mining periodic behaviors for moving objects. In: Proceedings of 16th ACM SIGKDD, Washington, DC, USA (2010)
Sadilek, A., Krumm, J.: Far Out: predicting long-term human mobility. In: Proceedings of Twenty-Sixth AAAI Conference on Artificial Intelligence, pp. 814–820 (2012)
Li, Z., Wang, J., Han, J.: Mining event periodicity from incomplete observations. In: Proceedings of 18th ACM SIGKDD, Beijing, China, (2012)
Oppenheim, A.V., Schafer, R.W., Buck, J.R.: Discrete-Time Signal Processing. Prentice Hall, Upper Saddler River, NJ (1999)
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© 2014 Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Baratchi, M., Meratnia, N., Havinga, P.J.M. (2014). Recognition of Periodic Behavioral Patterns from Streaming Mobility Data. In: Stojmenovic, I., Cheng, Z., Guo, S. (eds) Mobile and Ubiquitous Systems: Computing, Networking, and Services. MobiQuitous 2013. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 131. Springer, Cham. https://doi.org/10.1007/978-3-319-11569-6_9
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