MDTK: Bandwidth-Saving Framework for Distributed Top-k Similar Trajectory Query

  • Zhigang Zhang
  • Jiali Mao
  • Cheqing JinEmail author
  • Aoying Zhou
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10827)


During the past decade, with the popularity of smartphones and other mobile devices, big trajectory data is generated and stored in a distributed way. In this work, we focus on the DTW distance based top-k query over the distributed trajectory data. Processing such a query is challenging due to the limited network bandwidth and the computation overhead. To overcome these challenges, we propose a communication-saving framework MDTK (Multi-resolution based Distributed Top-K). MDTK sends the bounding envelopes of the reference trajectory from coarse to finer-grained resolutions and devises a level-increasing communication strategy to gradually tighten the proposed upper and lower bound. Then, distance bound based pruning strategies are imported to reduce both the computation and communication cost. Besides, we embed techniques including: indexing, early-stopping and cascade pruning, to improve the query efficiency. Extensive experiments on real datasets show that MDTK outperforms the state-of-the-art method.


Top-k query Communication cost DTW distance Trajectory data 



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 (No. ZF1213).


  1. 1.
    Cao, H., Wolfson, O., Trajcevski, G.: Spatio-temporal data reduction with deterministic error bounds. VLDB J. 15(3), 211–228 (2006)CrossRefGoogle Scholar
  2. 2.
    Chakrabarti, K., Keogh, E., Mehrotra, S., Pazzani, M.: Locally adaptive dimensionality reduction for indexing large time series databases. ACM Trans. Database Syst. (TODS) 27(2), 188–228 (2002)CrossRefGoogle Scholar
  3. 3.
    Chan, F.P., Fu, A.C., Yu, C.: Haar wavelets for efficient similarity search of time-series: with and without time warping. TKDE 15(3), 686–705 (2003)Google Scholar
  4. 4.
    Costa, C., Laoudias, C., Zeinalipour-Yazti, D., Gunopulos, D.: SmartTrace: finding similar trajectories in smartphone networks without disclosing the traces. In: Proceedings of the 27th ICDE, pp. 1288–1291 (2011)Google Scholar
  5. 5.
    Demetrios, Z.Y., Christos, L., Constandinos, C.: Crowdsourced trace similarity with smartphones. TKDE 25(6), 1240–1253 (2013)Google Scholar
  6. 6.
    Faloutsos, C., Ranganathan, M., Manolopoulos, Y.: Fast subsequence matching in time-series databases. In: Proceedings of the 1994 ACM SIGMOD, pp. 419–429 (1994)CrossRefGoogle Scholar
  7. 7.
    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 ICBD, pp. 551–560. IEEE (2015)Google Scholar
  8. 8.
    Jiangpeng, D., Jin, T., Xiaole, B., Zhaohui, S., Dong, X.: Mobile phone based drunk driving detection. In: Proceedings of the 2010 ICPCTH, pp. 1–8. IEEE (2010)Google Scholar
  9. 9.
    Kanth, K.V.R., Agrawal, D., Singh, A.K.: Dimensionality reduction for similarity searching in dynamic databases. In: Proceedings of the 1998 ACM SIGMOD, pp. 166–176 (1998)Google Scholar
  10. 10.
    Keogh, E.: Exact indexing of dynamic time warping. In: Proceedings of the 28th VLDB, pp. 406–417 (2002)CrossRefGoogle Scholar
  11. 11.
    Keogh, E.J., Chu, S., Hart, D.M., Pazzani, M.J.: An online algorithm for segmenting time series. In: Proceedings of the 2001 ICDM, pp. 289–296 (2001)Google Scholar
  12. 12.
    Papadopoulos, A.N., Manolopoulos, Y.: Distributed processing of similarity queries. Distrib. Parallel Databases 9(1), 67–92 (2001)CrossRefGoogle Scholar
  13. 13.
    Popivanov, I., Miller, R.J.: Similarity search over time-series data using wavelets. In: Proceedings of the 18th ICDE, pp. 212–221 (2002)Google Scholar
  14. 14.
    Rakthanmanon, T., Campana, B.J.L., Mueen, A.: Searching and mining trillions of time series subsequences under dynamic time warping. In: The 18th ACM SIGKDD, pp. 262–270 (2012)Google Scholar
  15. 15.
    Sakurai, Y., Yoshikawa, M., Faloutsos, C.: FTW: fast similarity search under the time warping distance. In: Proceedings of the 24th ACM PODS, pp. 326–337 (2005)Google Scholar
  16. 16.
    Xie, D., Li, F., Phillips, J.M.: Distributed trajectory similarity search. PVLDB 10(11), 1478–1489 (2017)Google Scholar
  17. 17.
    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. PVLDB 1(1), 586–597 (2008)Google Scholar
  18. 18.
    Yi, B., Faloutsos, C.: Fast time sequence indexing for arbitrary Lp norms. In: Proceedings of 26th VLDB, pp. 385–394 (2000)Google Scholar
  19. 19.
    Zheng, Y., Zhou, X. (eds.): Computing with Spatial Trajectories. Springer, New York (2011). Scholar
  20. 20.
    Zeinalipour-Yazti, D., Lin, S., Gunopulos, D.: Distributed spatio-temporal similarity search. In: Proceedings of the 2006 CIKM, pp. 14–23 (2006)Google Scholar
  21. 21.
    Zhang, Z., Wang, Y., Mao, J., Qiao, S., Jin, C., Zhou, A.: DT-KST: distributed top-k similarity query on big trajectory streams. In: Proceedings of the 22nd DASFAA, Part I, pp. 199–214 (2017)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Zhigang Zhang
    • 1
  • Jiali Mao
    • 1
  • Cheqing Jin
    • 1
    Email author
  • Aoying Zhou
    • 1
  1. 1.School of Data Science and EngineeringEast China Normal UniversityShanghaiChina

Personalised recommendations