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Distributed top-k similarity query on big trajectory streams

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Abstract

Recently, big trajectory data streams are generated in distributed environments with the popularity of smartphones and other mobile devices. Distributed top-k similarity query, which finds k trajectories that are most similar to a given query trajectory from all remote sites, is critical in this field. The key challenge in such a query is how to reduce the communication cost due to the limited network bandwidth resource. Although this query can be solved by sending the query trajectory to all the remote sites, in which the pairwise similarities are computed precisely. However, the overall cost, O(n · m), is huge when n or m is huge, where n is the size of query trajectory and m is the number of remote sites. Fortunately, there are some cheap ways to estimate pairwise similarity, which filter some trajectories in advance without precise computation. In order to overcome the challenge in this query, we devise two general frameworks, into which concrete distance measures can be plugged. The former one uses two bounds (the upper and lower bound), while the latter one only uses the lower bound. Moreover, we introduce detailed implementations of two representative distance measures, Euclidean and DTW distance, after inferring the lower and upper bound for the former framework and the lower bound for the latter one. Theoretical analysis and extensive experiments on real-world datasets evaluate the efficiency of proposed methods.

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References

  1. Dai J P, Teng J, Bai X, Shen Z H, Xuan D. Mobile phone based drunk driving detection. In: Proceedings of the 4th International Conference on Pervasive Computing Technologies for Healthcare. 2010, 1–8

    Google Scholar 

  2. Zeinalipour Yazti D, Laoudias C, Costa C, Vlachos M, Andreou M I, Gunopulos D. Crowdsourced trace similarity with smartphones. IEEE Transactions on Knowledge and Data Engineering, 2013, 25(6): 1240–1253

    Article  Google Scholar 

  3. Ding H, Trajcevski G, Scheuermann P. Efficient similarity join of large sets of moving object trajectories. In: Proceedings of the 15th International Conference on Temporal Representaion and Reasoning. 2008, 79–87

    Google Scholar 

  4. Ma C Y, Lu H, Shou L D, Chen G. KSQ: top-k similarity query on uncertain trajectories. IEEE Transactions on Knowledge and Data Engineering, 2013, 25(9): 2049–2062

    Article  Google Scholar 

  5. Skoumas G, Skoutas D, Vlachaki A. Efficient identification and approximation of k-nearest moving neighbors. In: Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. 2013, 264–273

    Google Scholar 

  6. Sacharidis D, Skoutas D, Skoumas G. Continuous monitoring of nearest trajectories. In: Proceedings of the 22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. 2014, 361–370

    Google Scholar 

  7. Yeh M Y, Wu K L, Yu P S, Chen M S. Leewave: level-wise distribution of wavelet coefficients for processing knn queries over distributed streams. Proceedings of the VLDB Endowment, 2008, 1(1): 586–597

    Article  Google Scholar 

  8. Hsu C C, Kung P H, Yeh M Y, Lin S D, Gibbons P B. Bandwidth-efficient distributed k-nearest-neighbor search with dynamic time warping. In: Proceedings of the 2015 IEEE International Conference on Big Data. 2015, 551–560

    Chapter  Google Scholar 

  9. Zhang Z G, Wang Y L, Mao J L, Qiao S J, Jin C Q, Zhou A Y. DTKST: distributed top-k similarity query on big trajectory streams. In: Proceedings of the 22nd International Conference on Database Systems for Advanced Applications. 2017, 199–214

    Chapter  Google Scholar 

  10. Faloutsos C, Ranganathan M, Manolopoulos Y. Fast subsequence matching in time-series databases. In: Proceedings of the 1994 ACM International Conference on Management of Data. 1994, 419–429

    Google Scholar 

  11. Kanth K V R, Agrawal D, Singh A K. Dimensionality reduction for similarity searching in dynamic databases. In: Proceedings of the 1998 ACM International Conference on Management of Data. 1998, 166–176

    Google Scholar 

  12. Popivanov I, Miller R J. Similarity search over time-series data using wavelets. In: Proceedings of the 18th International Conference on Data Engineering. 2002, 212–221

    Chapter  Google Scholar 

  13. Yi B K, Faloutsos C. Fast time sequence indexing for arbitrary Lp norms. In: Proceedings of the 26th International Conference on Very Large Data Bases. 2000, 385–394

    Google Scholar 

  14. Chakrabarti K, Keogh E, Mehrotra S, Pazzani M. Locally adaptive dimensionality reduction for indexing large time series databases. ACM Transactions on Database Systems, 2002, 27(2): 188–228

    Article  Google Scholar 

  15. Cao H, Wolfson O, Trajcevski G. Spatio-temporal data reduction with deterministic error bounds. The VLDB Journal, 2006, 15(3): 211–228

    Article  Google Scholar 

  16. Papadopoulos A N, Manolopoulos Y. Distributed processing of similarity queries. Distributed and Parallel Databases, 2001, 9(1): 67–92

    Article  MATH  Google Scholar 

  17. Kashyap S, Karras P. Scalable KNN search on vertically stored time series. In: Proceedings of the 17th ACM International Conference on Knowledge Discovery and Data Mining. 2011, 1334–1342

    Google Scholar 

  18. Vernica R, Carey M J, Li C. Efficient parallel set-similarity joins using mapreduce. In: Proceedings of the 16th ACM International Conference on Management of Data. 2010, 495–506

    Google Scholar 

  19. Kim Y, Shim K. Parallel top-k similarity join algorithms using mapreduce. In: Proceedings of the 28th IEEE International Conference on Data Engineering. 2012, 510–521

    Google Scholar 

  20. Yazti D Z, Lin S, Gunopulos D. Distributed spatio-temporal similarity search. In: Proceedings of the 2006 ACM International Conference on Information and Knowledge Management. 2006, 14–23

    Google Scholar 

  21. Costa C, Laoudias C, Yazti D Z, Gunopulos D. Smarttrace: finding similar trajectories in smartphone networks without disclosing the traces. In: Proceedings of the 27th International Conference on Data Engineering. 2011, 1288–1291

    Google Scholar 

  22. Chan K P, Fu A W C, Yu C T. Haar wavelets for efficient similarity search of time-series: with and without time warping. IEEE Transactions on Knowledge and Data Engineering, 2003, 15(3): 686–705

    Article  Google Scholar 

  23. Liu H P, Jin C Q, Zhou A Y. Popular route planning with travel cost estimation. In: Proceedings of the 21st International Conference on Database Systems for Advanced Applications. 2016, 403–418

    Chapter  Google Scholar 

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Acknowledgements

Our research is supported by the National Key Research and Development Program of China (2016YFB1000905), NSFC (61370101, 61532021, U1501252, U1401256 and 61402180), Shanghai Knowledge Service Platform Project (ZF1213).

Author information

Authors and Affiliations

  1. School of Data Science and Engineering, East China Normal University, Shanghai, 200062, China

    Zhigang Zhang, Xiaodong Qi, Yilin Wang, Cheqing Jin, Jiali Mao & Aoying Zhou

  2. Computer School, China West Normal University, Nanchong, 637009, China

    Jiali Mao

Authors
  1. Zhigang Zhang
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  2. Xiaodong Qi
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  3. Yilin Wang
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  4. Cheqing Jin
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  5. Jiali Mao
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  6. Aoying Zhou
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Corresponding author

Correspondence to Jiali Mao.

Additional information

Zhigang Zhang is currently working toward the PhD degree at the School of Data Science and Engineering, East China Normal University, China. His research interests include location based service, spatiotemporal data management and distributed computing.

Xiaodong Qi is currently working toward the PhD degree at the School of Data Science and Engineering, East China Normal University, China. His research interests include scientific data management and block chain.

Yilin Wang received her Bachelor degree of Computer Science and Technology from Northwestern Polytecnical University, China in 2015. She is a graduate student in the school of Software Engineering, East China Normal University. Her current research interests include data mining and location-based services.

Cheqing Jin is a professor on computer science at East China Normal University, China. He received Excellent Young Teacher Award by Fok Ying Tung Education Foundation. His main research interests include: streaming data management, location-based services, uncertain data management, data quality, and database benchmarking.

Jiali Mao is an associate professor at China West Normal University, China. She is currently working toward the PhD degree in the school of Data Science and Engineering, East China Normal University, China. Her current research interests include big data analysis and location-based services.

Aoying Zhou is a professor on computer science at East China Normal University, China, as well as the Dean of School of Data Science and Engineering (DaSE). His research interests include Web data management, data management for dataintensive computing, in-memory cluster computing, benchmarking for big data and performance.

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Zhang, Z., Qi, X., Wang, Y. et al. Distributed top-k similarity query on big trajectory streams. Front. Comput. Sci. 13, 647–664 (2019). https://doi.org/10.1007/s11704-018-7234-6

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