Zhenhui Li
Jiawei Han
ABSTRACT
Periodicity is a frequently happening phenomenon for moving objects. Finding periodic behaviors is essential to understanding object movements. However, periodic behaviors could be complicated, involving multiple interleaving periods, partial time span, and spatiotemporal noises and outliers.
In this paper, we address the problem of mining periodic behaviors for moving objects. It involves two sub-problems: how to detect the periods in complex movement, and how to mine periodic movement behaviors. Our main assumption is that the observed movement is generated from multiple interleaved periodic behaviors associated with certain reference locations. Based on this assumption, we propose a two-stage algorithm, Periodica, to solve the problem. At the first stage, the notion of observation spot is proposed to capture the reference locations. Through observation spots, multiple periods in the movement can be retrieved using a method that combines Fourier transform and autocorrelation. At the second stage, a probabilistic model is proposed to characterize the periodic behaviors. For a specific period, periodic behaviors are statistically generalized from partial movement sequences through hierarchical clustering. Empirical studies on both synthetic and real data sets demonstrate the effectiveness of our method.
Supplemental Material
- S. Bar-David, I. Bar-David, P. C. Cross, S. J. Ryan, and W. M. Getz. Methods for assessing movement path recursion with application to african buffalo in south africa. In Ecology, volume 90, 2009.Google Scholar
- C. Berberidis, W. G. Aref, M. J. Atallah, I. P. Vlahavas, and A. K. Elmagarmid. Multiple and partial periodicity mining in time series databases. In ECAI, 2002.Google Scholar
- H. Cao, D. W. Cheung, and N. Mamoulis. Discovering partial periodic patterns in discrete data sequences. In PAKDD, 2004.Google ScholarCross Ref
- H. Cao, N. Mamoulis, and D. W. Cheung. Discovery of periodic patterns in spatiotemporal sequences. IEEE Trans. Knowl. Data Eng., 19(4), 2007. Google ScholarDigital Library
- M. G. Elfeky, W. G. Aref, and A. K. Elmagarmid. Periodicity detection in time series databases. IEEE Trans. Knowl. Data Eng., 17(7), 2005. Google ScholarDigital Library
- M. G. Elfeky, W. G. Aref, and A. K. Elmagarmid. Warp: Time warping for periodicity detection. In ICDM, 2005. Google ScholarDigital Library
- J. Han, G. Dong, and Y. Yin. Efficient mining of partial periodic patterns in time series database. In ICDE, 1999.Google Scholar
- J. Han, W. Gong, and Y. Yin. Mining segment-wise periodic patterns in time-related databases. In KDD, 1998.Google ScholarDigital Library
- P. Indyk, N. Koudas, and S. Muthukrishnan. Identifying representative trends in massive time series data sets using sketches. In VLDB, 2000. Google ScholarDigital Library
- H. Jeung, Q. Liu, H. T. Shen, and X. Zhou. A hybrid prediction model for moving objects. In ICDE, 2008. Google ScholarDigital Library
- S. Ma and J. L. Hellerstein. Mining partially periodic event patterns with unknown periods. In ICDE, 2001. Google ScholarDigital Library
- N. Mamoulis, H. Cao, G. Kollios, M. Hadjieleftheriou, Y. Tao, and D. W. Cheung. Mining, indexing, and querying historical spatiotemporal data. In KDD, 2004. Google ScholarDigital Library
- M. Vlachos, P. S. Yu, and V. Castelli. On periodicity detection and structural periodic similarity. In SDM, 2005.Google ScholarCross Ref
- C. Wang and S. Parthasarathy. Summarizing itemset patterns using probabilistic models. In KDD, 2006. Google ScholarDigital Library
- W. Wang, J. Yang, and P. S. Yu. Meta-patterns: Revealing hidden periodic patterns. In ICDM, 2001. Google ScholarDigital Library
- B. J. Worton. Kernel methods for estimating the utilization distribution in home-range studies. In Ecology, volume 70, 1989.Google Scholar
- Y. Xia, Y. Tu, M. Atallah, and S. Prabhakar. Reducing data redundancy in location-based services. In GeoSensor, 2006.Google Scholar
- X. Yan, H. Cheng, J. Han, and D. Xin. Summarizing itemset patterns: a profile-based approach. In KDD, 2005. Google ScholarDigital Library
- J. Yang, W. Wang, and P. S. Yu. Mining asynchronous periodic patterns in time series data. In KDD, 2000. Google ScholarDigital Library
- J. Yang, W. Wang, and P. S. Yu. Infominer: mining surprising periodic patterns. In KDD, 2001. Google ScholarDigital Library
- J. Yang, W. Wang, and P. S. Yu. Infominer+: Mining partial periodic patterns with gap penalties. In ICDM, 2002. Google ScholarDigital Library
- M. Zhang, B. Kao, D. W.-L. Cheung, and K. Y. Yip. Mining periodic patterns with gap requirement from sequences. In SIGMOD Conference, 2005. Google ScholarDigital Library
Index Terms
- Mining periodic behaviors for moving objects
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