Journal of Computing Science and Engineering

  • 제5권4호
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  • Pages.346-353
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  • 2011
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  • 1976-4677(pISSN)
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  • 2093-8020(eISSN)
한국정보과학회 (Korean Institute of Information Scientists and Engineers)
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Online Clustering Algorithms for Semantic-Rich Network Trajectories

  • Roh, Gook-Pil (Department of Computer Science and Engineering, Pohang University of Science and Technology (POSTECH)) ;
  • Hwang, Seung-Won (Department of Computer Science and Engineering, Pohang University of Science and Technology (POSTECH))
  • 투고 : 2011.07.01
  • 심사 : 2011.08.26
  • 발행 : 2011.12.30
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초록

With the advent of ubiquitous computing, a massive amount of trajectory data has been published and shared in many websites. This type of computing also provides motivation for online mining of trajectory data, to fit user-specific preferences or context (e.g., time of the day). While many trajectory clustering algorithms have been proposed, they have typically focused on offline mining and do not consider the restrictions of the underlying road network and selection conditions representing user contexts. In clear contrast, we study an efficient clustering algorithm for Boolean + Clustering queries using a pre-materialized and summarized data structure. Our experimental results demonstrate the efficiency and effectiveness of our proposed method using real-life trajectory data.

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참고문헌

  1. C. S. Jensen, D. Lin, and B. C. Ooi, "Query and update efficient B+-tree based indexing of moving objects," Proceedings of the Thirtieth International Conference on Very Large Data Bases, Toronto, Canada, 2004, pp. 768-779.
  2. H. Hu, J. Xu, and D. L. Lee, "A generic framework for monitoring continuous spatial queries over moving objects," Proceedings of the ACM SIGMOD International Conference on Management of Data, Baltimore, MD, 2005, pp. 479-490.
  3. X. Xiong, M. F. Mokbel, and W. G. Aref, "SEA-CNN: scalable processing of continuous K-nearest neighbor queries in spatiotemporal databases," Proceedings of the 21st International Conference on Data Engineering, Tokyo, Japan, 2005, pp. 643-654.
  4. S. Chen, B. C. Ooi, K. L. Tan, and M. A. Nascimento, "ST2Btree: a self-tunable spatio-temporal B+-tree index for moving objects," Proceedings of the ACM SIGMOD International Conference on Management of Data, Vancouver, BC, 2008, pp. 29- 42.
  5. V. W. Zheng, Y. Zheng, X. Xie, and Q. Yang, "Collaborative location and activity recommendations with GPS history data," Proceedings of the 19th International World Wide Web Conference, Raleigh, NC, 2010, pp. 1029-1038.
  6. Y. Zheng, L. Zhang, X. Xie, and W. Y. Ma, "Mining interesting locations and travel sequences from GPS trajectories," Proceedings of the 18th International Conference on World Wide Web, Madrid, Spain, 2009, pp. 791-800.
  7. G. P. Roh and S. W. Hwang, "NNCluster: an efficient clustering algorithm for road network trajectories," Database Systems for Advanced Applications. Lecture Notes in Computer Science Vol. 5982, H. Kitagawa, Y. Ishikawa, Q. Li, and C. Watanabe, Eds., Heidelberg, Germany: Springer Berlin, 2010, pp. 47-61.
  8. J. Lou, Q. Liu, T. Tan, and W. Hu, "Semantic interpretation of object activities in a surveillance system," Proceedings of the 16th International Conference on Pattern Recognition, Quebec, QC, Canada, 2002, pp. 777-780.
  9. I. N. Junejo, O. Javed, and M. Shah, "Multi feature path modeling for video surveillance," Proceedings of the 17th International Conference on Pattern Recognition, Cambridge, UK, 2004, pp. 716-719.
  10. J. Gudmundsson, M. v. Kreveld, and B. Speckmann, "Efficient detection of motion patterns in spatio-temporal data sets," Proceedings of the 12th annual ACM international workshop on Geographic information systems, Washington DC, 2004, pp. 250- 257.
  11. H. Cao and O. Wolfson, "Nonmaterialized motion information in transport networks," Database Theory: ICDT 2005. Lecture Notes in Computer Science Vol. 3363, T. Eiter and L. Libkin, Eds., Heidelberg, Germany: Springer Berlin, 2005, pp. 173-188.
  12. J. G. Lee, J. Han, and K. Y. Whang, "Trajectory clustering: a partition- and-group framework," Proceedings of the ACM SIGMOD International Conference on Management of Data, Beijing, China, 2007, pp. 593-604.
  13. B. Lin and J. Su, "One way distance: for shape based similarity search of moving object trajectories," GeoInformatica, vol. 12, no. 2, pp. 117-142, 2008. https://doi.org/10.1007/s10707-007-0027-y
  14. M. Vlachos, G. Kollios, and D. Gunopulos, "Discovering similar multidimensional trajectories," Proceedings of the 18th International Conference on Data Engineering, San Jose, CA, 2002, pp. 673-684.
  15. L. Chen and R. Ng, "On the marriage of Lp-norms and edit distance," Proceedings of the Thirtieth International Conference on Very Large Data Bases, Toronto, Canada, 2004, pp. 792-803.
  16. L. Chen, M. T. Ozsu, and V. Oria, "Robust and fast similarity search for moving object trajectories," Proceedings of the ACM SIGMOD International Conference on Management of Data, Baltimore, MD, 2005, pp. 491-502.
  17. H. Jeung, M. L. Yiu, X. Zhou, C. S. Jensen, and H. T. Shen, "Discovery of convoys in trajectory databases," Proceedings of the VLDB Endowment, vol. 1, no. 1, pp. 1068-1080, 2008. https://doi.org/10.14778/1453856.1453971
  18. S. Gaffney and P. Smyth, "Trajectory clustering with mixtures of regression models," Proceedings of the Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Diego, CA, 1999, pp. 63-72.
  19. C. Li, M. Wang, L. Lim, H. Wang, and K. C. C. Chang, "Supporting ranking and clustering as generalized order-by and groupby," Proceedings of the ACM SIGMOD International Conference on Management of Data, Beijing, China, 2007, pp. 127-138.
  20. S. Brakatsoulas, D. Pfoser, R. Salas, and C. Wenk, "On mapmatching vehicle tracking data," Proceedings of the 31st International Conference on Very Large Data Bases, Trondheim, Norway, 2005, pp. 853-864.
  21. R. M. Karp, "Reducibility among combinatorial problems," Complexity of Computer Computations: Proceedings, R. E. Miller, J. W. Thatcher, United States Office of Naval Research Mathematics Program, IBM World Trade Corporation, and International Business Machines Corporation Mathematical Sciences Department, Eds., New York, NY: Plenum Press, 1972, pp. 85-103.
  22. Y. Leung, J. S. Zhang, and Z. B. Xu, "Clustering by scale-space filtering," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 22, no. 12, pp. 1396-1410, 2000. https://doi.org/10.1109/34.895974
  23. D. L. Davies and D. W. Bouldin, "Cluster separation measure," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. PAMI-1, no. 2, pp. 224-227, 1979. https://doi.org/10.1109/TPAMI.1979.4766909

피인용 문헌

  1. Intra graph clustering using collaborative similarity measure vol.33, pp.4, 2015, https://doi.org/10.1007/s10619-014-7170-x
  2. Efficient Similarity Search Based on Semantic Trajectories in Road Networks vol.23, pp.4, 2018, https://doi.org/10.1007/s11859-018-1333-y