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VID Join: Mapping Trajectories to Points of Interest to Support Location-Based Services

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Abstract

Variable influence duration (VID) join is a novel spatio-temporal join operation between a set T of trajectories and a set P of spatial points. Here, trajectories are traveling histories of moving objects (e.g., travelers), and spatial points are points of interest (POIs, e.g., restaurants). VID join returns all pairs of (τ s , p) if τ s is spatially close to p for a long period of time, where τ s is a segment of trajectory τ ∈ T and p ∈ P. Each returned (τ s , p) implies that the moving object associated with τ s stayed at p (e.g., having dinner at a restaurant). Such information is useful in many aspects, such as targeted advertising, social security, and social activity analysis. The concepts of influence and influence duration are introduced to measure the spatial closeness between τ and p, and the time spanned, respectively. Compared to the conventional spatio-temporal join, the VID join is more challenging since the join condition varies for different POIs, and the additional temporal requirement cannot be indexed effectively. To process the VID join efficiently, three algorithms are developed and several optimization techniques are applied, including spatial duplication reuse and time duration based pruning. The performance of the developed algorithms is verified by extensive experiments on real spatial data.

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References

  1. Zheng Y, Xie X, Ma W Y. Geolife: A collaborative social networking service among user, location and trajectory. IEEE Data Engineering Bulletin, 2010, 33(2): 32–39.

  2. Yiu M L, Mamoulis N, Karras P. Common influence join: A natural join operation for spatial pointsets. In Proc. the 24th ICDE, April 2008, pp. 100–109.

  3. Li Q, Zheng Y, Xie X, Chen Y, Liu W, Ma W Y. Mining user similarity based on location history. In Proc. the 16th ACM SIGSPATIAL GIS, November 2008, pp. 34:1–34:10.

  4. Giannotti F, Nanni M, Pinelli F, Pedreschi D. Trajectory pattern mining. In Proc. the 13th KDD, August 2007, pp. 330–339.

  5. Jeung H, Shen H T, Zhou X. Convoy queries in spatio-temporal databases. In Proc. the 24th ICDE, April 2008, pp. 1457–1459.

  6. Jeung H, Liu Q, Shen H T, Zhou X. A hybrid prediction model for moving objects. In Proc. the 24th ICDE, April 2008, pp. 70–79.

  7. Jeung H, Yiu M L, Zhou X, Jensen C S, Shen H T. Discovery of convoys in trajectory databases. Proceedings of the VLDB Endowment, 2008, 1(1): 1068–1080.

  8. Lee J G, Han J, Whang K Y. Trajectory clustering: A partition-and-group framework. In Proc. ACM SIGMOD, June 2007, pp. 593–604.

  9. Shang S, Ding R, Zheng K, Jensen C S, Kalnis P, Zhou X. Personalized trajectory matching in spatial networks. VLDB J., 2014, 23(3): 449–468.

  10. Shang S, Ding R, Yuan B, Xie K, Zheng K, Kalnis P. User oriented trajectory search for trip recommendation. In Proc. the 15th EDBT, March 2012, pp. 156–167.

  11. Zheng K, Zheng Y, Yuan N J, Shang S. On discovery of gathering patterns from trajectories. In Proc. the 29th ICDE, April 2013, pp. 242–253.

  12. Šaltenis S, Jensen C S, Leutenegger S T, Lopez M A. Indexing the positions of continuously moving objects. ACM SIGMOD Record, 2000, 29(2): 331–342.

  13. Šaltenis S, Jensen C S. Indexing of moving objects for location-based services. In Proc. the 18th ICDE, February 26-March 1, 2002, pp. 463–472.

  14. Pfoser D, Jensen C S, Theodoridis Y. Novel approaches in query processing for moving object trajectories. In Proc. the 26th VLDB, September 2000, pp. 395–406.

  15. Chakka V P, Everspaugh A, Patel J M. Indexing large trajectory data sets with SETI. In Proc. the 1st CIDR, January 2003.

  16. Arumugam S, Jermaine C. Closest-point-of-approach join for moving object histories. In Proc. the 22nd ICDE, April 2006, Article No. 86.

  17. Tao Y, Papadias D, Shen Q. Continuous nearest neighbor search. In Proc. the 28th VLDB, August 2002, pp. 287–298.

  18. Patel J M, DeWitt D J. Partition based spatial-merge join. ACM SIGMOD Record, 1996, 25(2): 259–270.

  19. Xie K, Deng K, Zhou X. From trajectories to activities: A spatio-temporal join approach. In Proc. LBSN, November 2009, pp. 25–32.

  20. Jacox E H, Samet H. Spatial join techniques. ACM Trans. Database Syst., 2007, 32(1): Article No. 7.

  21. Günther O. Efficient computation of spatial joins. In Proc. the 9th ICDE, April 1993, pp. 50–59.

  22. Brinkhoff T, Kriegel H P, Seeger B. Efficient processing of spatial joins using R-trees. In Proc. ACM SIGMOD, May 1993, pp. 237–246.

  23. Kim S W, Cho W S, Lee M J, Whang K Y. A new algorithm for processing joins using the multilevel grid file. In Proc. the 4th DASFAA, April 1995, pp. 115–123.

  24. Huang Y W, Jing N, Rundensteiner E A. Spatial joins using R-trees: Breadth-first traversal with global optimizations. In Proc. the 23rd VLDB, August 1997, pp. 396–405.

  25. Zhou X, Abel D J, Truffet D. Data partitioning for parallel spatial join processing. In Proc. the 5th SSTD, July 1997, pp. 178–196.

  26. Koudas N, Sevcik K C. Size separation spatial join. In Proc. ACM SIGMOD, May 1997, pp. 324–335.

  27. Dittrich J P, Seeger B. Data redundancy and duplicate detection in spatial join processing. In Proc. the 16th ICDE, February 28–March 3, 2000, pp. 535–546.

  28. Arge L, Procopiuc O, Ramaswamy S, Suel T, Vitter J S. Scalable sweeping-based spatial join. In Proc. the 24th VLDB, Aug. 1998, pp. 570–581.

  29. Douglas D H, Peucker T K. Algorithms for the reduction of the number of points required to represent a digitized line or its caricature. Cartographica: The International Journal for Geographic Information and Geovisualization, 1973, 10(2): 112–122.

  30. Cao H, Mamoulis N, Cheung D W. Mining frequent spatio-temporal sequential patterns. In Proc. the 5th ICDM, November 2005, pp. 82–89.

  31. Kalnis P, Mamoulis N, Bakiras S. On discovering moving clusters in spatio-temporal data. In Proc. the 9th SSTD, August 2005, pp. 364–381.

  32. Alvares L O, Bogorny V, Kuijpers B, de Macêdo J A F, Moelans B, Vaisman A A. A model for enriching trajectories with semantic geographical information. In Proc. the 15th ACM GIS, Nov. 2007, pp. 22:1–22:8.

  33. Xue A Y, Qi J, Xie X, Zhang R, Huang J, Li Y. Solving the data sparsity problem in destination prediction. VLDB J., 2015, 24(2): 219–243.

  34. Xue A Y, Zhang R, Zheng Y, Xie X, Huang J, Xu Z. Destination prediction by sub-trajectory synthesis and privacy protection against such prediction. In Proc. the 29th ICDE, April 2013, pp. 254–265.

  35. Yuan J, Zheng Y, Xie X, Sun G. Driving with knowledge from the physical world. In Proc. the 17th SIGKDD, August 2011, pp. 316–324.

  36. Horvitz E, Krumm J. Some help on the way: Opportunistic routing under uncertainty. In Proc. the 14th ACM Ubicomp, September 2012, pp. 371–380.

  37. Fortune S. A sweepline algorithm for Voronoi diagrams. Algorithmica, 1987, 2: 153–174.

  38. Li F, Cheng D, Hadjieleftheriou M, Kollios G, Teng S H. On trip planning queries in spatial databases. In Proc. the 9th SSTD, August 2005, pp. 273–290.

  39. Chen Y, Jiang K, Zheng Y, Li C, Yu N. Trajectory simplification method for location-based social networking services. In Proc. LBSN, November 2009, pp. 33–40.

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Authors and Affiliations

  1. Department of Computer Science, China University of Petroleum, Beijing, 102249, China

    Shuo Shang

  2. School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, QLD, 4072, Australia

    Kexin Xie

  3. School of Computer Science and Technology, Soochow University, Suzhou, 215006, China

    Kai Zheng

  4. Commonwealth Scientific and Industrial Research Organisation, Kenmore, QLD, 4069, Australia

    Jiajun Liu

  5. Key Laboratory of Data Engineering and Knowledge Engineering, Renmin University of China, Beijing, 100080, China

    Ji-Rong Wen

Authors
  1. Shuo Shang
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  2. Kexin Xie
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  3. Kai Zheng
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  4. Jiajun Liu
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  5. Ji-Rong Wen
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Corresponding author

Correspondence to Kexin Xie.

Additional information

This work is partly supported by the National Natural Science Foundation of China under Grant No. 61402532, the Science Foundation of China University of Petroleum (Beijing) under Grant No. 2462013YJRC031, and the Excellent Talents of Beijing Program under Grant No. 2013D009051000003.

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Shang, S., Xie, K., Zheng, K. et al. VID Join: Mapping Trajectories to Points of Interest to Support Location-Based Services. J. Comput. Sci. Technol. 30, 725–744 (2015). https://doi.org/10.1007/s11390-015-1557-7

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  • DOI: https://doi.org/10.1007/s11390-015-1557-7

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