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Snapshot and continuous points-based trajectory search

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Abstract

Trajectory data capture the traveling history of moving objects such as people or vehicles. With the proliferation of GPS and tracking technologies, huge volumes of trajectories are rapidly generated and collected. Under this, applications such as route recommendation and traveling behavior mining call for efficient trajectory retrieval. In this paper, we first focus on distance-to-points trajectory search; given a collection of trajectories and a set query points, the goal is to retrieve the top-k trajectories that pass as close as possible to all query points. We advance the state-of-the-art by combining existing approaches to a hybrid nearest neighbor-based method while also proposing an alternative, more efficient spatial range-based approach. Second, we investigate the continuous counterpart of distance-to-points trajectory search where the query is long-standing and the set of returned trajectories needs to be maintained whenever updates occur to the query and/or the data. Third, we propose and study two practical variants of distance-to-points trajectory search, which take into account the temporal characteristics of the searched trajectories. Through an extensive experimental analysis with real trajectory data, we show that our range-based approach outperforms previous methods by at least one order of magnitude for the snapshot and up to several times for the continuous version of the queries.

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Notes

  1. Under the similarity-based definition of DTS in [2], IKNN sets empty “slots” to 0.

  2. In the future, we plan to investigate variable ξ j values based on current radius r j and the trajectory point density around q j , inspired by determining δ j value in [2].

  3. http://research.microsoft.com/en-us/projects/geolife/

References

  1. Böhm C, Berchtold S, Keim DA (2001) Searching in high-dimensional spaces: index structures for improving the performance of multimedia databases. ACM Comput Surv 33(3):322–373

    Article  Google Scholar 

  2. Chen Z, Shen HT, Zhou X, Zheng Y, Xie X (2010) Searching trajectories by locations: an efficiency study. In: SIGMOD, pp 255–266

  3. Fagin R, Lotem A, Naor M (2001) Optimal aggregation algorithms for middleware. In: PODS, pp 102–113

  4. Frentzos E, Gratsias K, Pelekis N, Theodoridis Y (2007) Algorithms for nearest neighbor search on moving object trajectories. GeoInformatica 11(2):159–193

    Article  Google Scholar 

  5. Güntzer U, Balke W, Kießling W (2001) Towards efficient multi-feature queries in heterogeneous environments. In: ITCC, pp 622–628

  6. Güntzer U, Balke WT, Kießling W (2000) Optimizing multi-feature queries for image databases. In: VLDB, pp 419–428

  7. Hjaltason GR, Samet H (1999) Distance browsing in spatial databases. ACM Trans Database Syst 24(2):265–318

    Article  Google Scholar 

  8. Ilyas IF, Beskales G, Soliman MA (2008) A survey of top-k query processing techniques in relational database systems. ACM Comput Surv 40(4)

  9. Jagadish HV, Ooi BC, Tan K, Yu C, Zhang R (2005) iDistance: an adaptive b +-tree based indexing method for nearest neighbor search. ACM Trans Database Syst 30 (2):364–397

    Article  Google Scholar 

  10. Lee JG, Han J, Whang KY (2007) Trajectory clustering: a partition-and-group framework. In: SIGMOD, pp 593–604

  11. Li X, Han J, Lee JG, Gonzalez H (2007) Traffic density-based discovery of hot routes in road networks. In: SSTD, pp 441–459

  12. Mouratidis K, Hadjieleftheriou M, Papadias D (2005) Conceptual partitioning: an efficient method for continuous nearest neighbor monitoring. In: SIGMOD, pp 634–645

  13. Papadias D, Tao Y, Mouratidis K, Hui CK (2005) Aggregate nearest neighbor queries in spatial databases. ACM Trans Database Syst 30(2):529–576

    Article  Google Scholar 

  14. Pfoser D, Jensen CS, Theodoridis Y (2000) Novel approaches to the indexing of moving object trajectories. In: VLDB, pp 395–406

  15. Qi S, Bouros P, Sacharidis D, Mamoulis N (2015) Efficient point-based trajectory search. In: SSTD, pp 179–196

  16. Roussopoulos N, Kelley S, Vincent F (1995) Nearest neighbor queries. In: SIGMOD, pp 71–79

  17. Song Z, Roussopoulos N (2001) K-nearest neighbor search for moving query point. In: SSTD, pp 79–96

  18. Tang LA, Zheng Y, Xie X, Yuan J, Yu X, Han J (2011) Retrieving k-nearest neighboring trajectories by a set of point locations. In: SSTD, pp 223–241

  19. Tao Y, Yi K, Sheng C, Kalnis P (2009) Quality and efficiency in high dimensional nearest neighbor search. In: SIGMOD, pp 563–576

  20. Xiong X, Mokbel MF, Aref WG (2005) Sea-cnn: scalable processing of continuous k-nearest neighbor queries in spatio-temporal databases. In: ICDE, pp 643–654

  21. Yu X, Pu KQ, Koudas N (2005) Monitoring k-nearest neighbor queries over moving objects. In: ICDE, pp 631–642

  22. Zhang J, Zhu M, Papadias D, Tao Y, Lee DL (2003) Location-based spatial queries. In: SIGMOD, pp 443–454

  23. Zheng Y (2015) Trajectory data mining: an overview. ACM Trans Intell Syst Tech 6(3):29:1–29:41

    Article  Google Scholar 

  24. Zheng Y, Li Q, Chen Y, Xie X, Ma W (2008) Understanding mobility based on GPS data. In: Ubicomp, pp 312–321

  25. Zheng Y, Xie X, Ma W (2010) Geolife: a collaborative social networking service among user, location and trajectory. IEEE Data Eng Bull 33(2):32–39

    Google Scholar 

  26. Zheng Y, Zhang L, Xie X, Ma WY (2009) Mining interesting locations and travel sequences from gps trajectories. In: WWW, pp 791–800

  27. Zheng Y, Zhou X (eds) (2011) Computing with spatial trajectories. Springer

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Acknowledgments

Work supported by grant 17205015 from Hong Kong RGC.

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Authors and Affiliations

  1. Department of Computer Science, The University of Hong Kong, Hong Kong, China

    Shuyao Qi & Nikos Mamoulis

  2. Faculty of Informatics, Technische Universität Wien, Vienna, Austria

    Dimitris Sacharidis

  3. Department of Computer Science, Aarhus University, Aarhus, Denmark

    Panagiotis Bouros

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  1. Shuyao Qi
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  2. Dimitris Sacharidis
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  3. Panagiotis Bouros
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  4. Nikos Mamoulis
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Correspondence to Shuyao Qi.

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Qi, S., Sacharidis, D., Bouros, P. et al. Snapshot and continuous points-based trajectory search. Geoinformatica 21, 669–701 (2017). https://doi.org/10.1007/s10707-016-0267-9

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