Journal of Information Processing Systems

Korea Information Processing Society (한국정보처리학회)
권호 표지
DOI QR코드

DOI QR Code

Mining Spatio-Temporal Patterns in Trajectory Data

  • Kang, Ju-Young (Department of Computer Science and Engineering, Ewha Womans University) ;
  • Yong, Hwan-Seung (Department of Computer Science and Engineering, Ewha Womans University)
  • Received : 2010.08.20
  • Accepted : 2010.10.29
  • Published : 2010.12.31
Download PDF
⟨ Previous Next ⟩

Abstract

Spatio-temporal patterns extracted from historical trajectories of moving objects reveal important knowledge about movement behavior for high quality LBS services. Existing approaches transform trajectories into sequences of location symbols and derive frequent subsequences by applying conventional sequential pattern mining algorithms. However, spatio-temporal correlations may be lost due to the inappropriate approximations of spatial and temporal properties. In this paper, we address the problem of mining spatio-temporal patterns from trajectory data. The inefficient description of temporal information decreases the mining efficiency and the interpretability of the patterns. We provide a formal statement of efficient representation of spatio-temporal movements and propose a new approach to discover spatio-temporal patterns in trajectory data. The proposed method first finds meaningful spatio-temporal regions and extracts frequent spatio-temporal patterns based on a prefix-projection approach from the sequences of these regions. We experimentally analyze that the proposed method improves mining performance and derives more intuitive patterns.

Keywords

References

  1. G. Gidofalvi, T. Pedersen, “Mining Long, Sharable Patterns in Trajectories of Moving Objects”, Proceedings of STDBM, 2006, pp.49-58.
  2. J. Kang, and H. Yong, “Spatio-temporal discretization for sequential pattern mining”, Proceedings of International Conference Ubiquitous Information Management and Communication, 2008, pp.218-224.
  3. I. Tsoukatos, and D. Gunopulos, “Efficient mining of spatiotemporal patterns”, Proceedings of International Symposium on in Spatial and Temporal Databases, 2001, pp.425-442. https://doi.org/10.1007/3-540-47724-1_22
  4. D. Birant and A. Kut. “ST-DBSCAN: An algorithm for clustering spatial-temporal data”, Data and Knowledge Engineering, Vol.60, No.1, 2007, pp.208-221. https://doi.org/10.1016/j.datak.2006.01.013
  5. V. S. Tseng, K.W. Lin, “Mining Temporal Moving Patterns in Object Tracking Sensor Networks”, Proceedings of International Workshop on Ubiquitous Data Management, 2005, pp.105-112. https://doi.org/10.1109/UDM.2005.16
  6. G. Yavas, D. Katsaros, O. Ulusoy, and Y. Manolopoulos. “A data mining approach for location prediction in mobile environments”, Data and Knowledge Engineering. Vol.54, No.2, 2005, pp.121-146. https://doi.org/10.1016/j.datak.2004.09.004
  7. N. Mamoulis, H. Cao, G. Kollios, M. Hadjieleftheriou, Y. Tao, and D. W. Cheung, “Mining, indexing, and querying historical spatiotemporal data”, Proceedings of 10th International Conference on Knowledge Discovery and Data Mining, 2004, pp.236-245.
  8. H. Cao, N. Mamoulis, and D.W. Cheung, “Mining frequent spatio-temporal sequential patterns”, Proceedings of Data Mining, 2005, pp.82-89.
  9. H. Cao, N. Mamoulis, D.W. Cheung, “Discovery of Periodic Patterns in Spatiotemporal Sequences”, IEEE. Transactions on Knowledge and Data Engineering, Vol.19, No.4, 2007, pp.453-467. https://doi.org/10.1109/TKDE.2007.1002
  10. F. Giannotti, M. Nanni, D. Pedreschi, and F. Pinelli, “Trajectory Pattern Mining”, Proceedings International Conference on Knowledge Discovery and Data Mining, 2007, pp.330-339.
  11. A. Monreale, F. Pinelli, R. Trasarti and F. Giannotti, “WhereNext: a Location Predictor on Trajectory Pattern Mining,” Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2009, pp.637-646. https://doi.org/10.1145/1557019.1557091
  12. J. Pei, J. Han, B. Mortazavi-Asl, H. Pinto, Q. Chen, U. Dayal, and M.C. Hsu. “PrefixSpan: Mining sequential patterns efficiently by prefix-projected pattern growth”, Proceedings of 17th International Conference on Data Engineering, 2001, pp.215-224.
  13. H. Cao, O. Wolfson, and G. Trajcevski, “Spatio-temporal data reduction with deterministic error bounds”, The VLDB Journal, Vol.15, No.3, 2006, pp.221-228. https://doi.org/10.1007/s00778-005-0163-7
  14. T. Zhang, R. Ramakrishnan, and M. Livny, “BIRCH: An efficient data clustering method for very large databases”, Proceedings of ACM SIGMOD International Conference on Management of Data, 1996, pp.103-114. https://doi.org/10.1145/233269.233324
  15. C. Kim, J. Lim, R. T. Ng, and K. Shim, “SQUIRE: Sequential Pattern Mining with Quantities”, Journal of Systems and Software, Vol.80, No.10, 2007, pp.1726-1745. https://doi.org/10.1016/j.jss.2006.12.562
  16. T. Tzouramanis, M. Vassilakopoulos, and Y. Manolopoulos. “On the generation of time-evolving regional data”, Geoinformatica, Vol.6, No.3, 2002, pp.207-231. https://doi.org/10.1023/A:1019705618917
  17. R. Srikant and R. Agrawal. “Mining Sequential Patterns: Generalizations and Performance Improvements”, Proceedings of the 5th International Conference on Extending Database Technology, 1996, pp.3-17. https://doi.org/10.1007/BFb0014140

Cited by

  1. A big data approach for logistics trajectory discovery from RFID-enabled production data vol.165, 2015, https://doi.org/10.1016/j.ijpe.2015.02.014
  2. Sequential pattern mining of geo-tagged photos with an arbitrary regions-of-interest detection method vol.41, pp.7, 2014, https://doi.org/10.1016/j.eswa.2013.10.057
  3. Dense traffic flow patterns mining in bi-directional road networks using density based trajectory clustering vol.11, pp.3, 2017, https://doi.org/10.1007/s11634-016-0256-8
  4. A service scenario generation scheme based on association rule mining for elderly surveillance system in a smart home environment vol.25, pp.7, 2012, https://doi.org/10.1016/j.engappai.2012.02.003
  5. Hierarchical Spatio-Temporal Pattern Discovery and Predictive Modeling vol.28, pp.4, 2016, https://doi.org/10.1109/TKDE.2015.2507570
  6. Density and Frequency-Aware Cluster Identification for Spatio-Temporal Sequence Data vol.93, pp.1, 2017, https://doi.org/10.1007/s11277-016-3937-x
  7. A framework of spatio-temporal trajectory simplification methods 2017, https://doi.org/10.1080/13658816.2017.1290250
  8. Smart CDSS: integration of Social Media and Interaction Engine (SMIE) in healthcare for chronic disease patients vol.74, pp.14, 2015, https://doi.org/10.1007/s11042-013-1668-5
  9. Spatiotemporal Modeling and Analysis—Introduction and Overview vol.26, pp.3, 2012, https://doi.org/10.1007/s13218-012-0215-2
  10. An efficient approach to understanding social evolution of location-focused online communities in location-based services 2017, https://doi.org/10.1007/s00500-017-2627-2
  11. An Automatic Extraction Method of Coach Operation Information from Historical Trajectory Data vol.2019, pp.2042-3195, 2019, https://doi.org/10.1155/2019/3634942