Distributed trajectory similarity search

Dong Xie, Feifei Li, Jeff M. Phillips

Research output: Contribution to journalConference articlepeer-review

95 Scopus citations


Mobile and sensing devices have already become ubiquitous. They have made tracking moving objects an easy task. As a result, mobile applications like Uber and many IoT projects have generated massive amounts of trajectory data that can no longer be processed by a single machine efficiently. Among the typical query operations over trajectories, similarity search is a common yet expensive operator in querying trajectory data. It is useful for applications in different domains such as traffic and transportation optimizations, weather forecast and modeling, and sports analytics. It is also a fundamental operator for many important mining operations such as clustering and classification of trajectories. In this paper, we propose a distributed query framework to process trajectory similarity search over a large set of trajectories. We have implemented the proposed framework in Spark, a popular distributed data processing engine, by carefully considering different design choices. Our query framework supports both the Hausdorffdistance the Fréchet distance. Extensive experiments have demonstrated the excellent scalability and query efficiency achieved by our design, compared to other methods and design alternatives.

Original languageEnglish (US)
Pages (from-to)1478-1489
Number of pages12
JournalProceedings of the VLDB Endowment
Issue number11
StatePublished - Aug 1 2017
Event43rd International Conference on Very Large Data Bases, VLDB 2017 - Munich, Germany
Duration: Aug 28 2017Sep 1 2017

All Science Journal Classification (ASJC) codes

  • Computer Science (miscellaneous)
  • General Computer Science


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