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

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

Abstract

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.

Keywords

Top-k query Communication cost DTW distance Trajectory data 

Notes

Acknowledgement

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).

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

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

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