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Distributed spatio-temporal similarity search

Published: 06 November 2006 Publication History

Abstract

In this paper we introduce the distributed spatio-temporal similarity search problem: given a query trajectory Q, we want to find the trajectories that follow a motion similar to Q, when each of the target trajectories is segmented across a number of distributed nodes. We propose two novel algorithms, UB-K and UBLB-K, which combine local computations of lower and upper bounds on the matching between the distributed subsequences and Q. Such an operation generates the desired result without pulling together all the distributed subsequences over the fundamentally expensive communication medium. Our solutions find applications in a wide array of domains, such as cellular networks, wild life monitoring and video surveillance. Our experimental evaluation using realistic data demonstrates that our framework is both efficient and robust to a variety of conditions.

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  • (2020)Distributed Subtrajectory Join on Massive DatasetsACM Transactions on Spatial Algorithms and Systems10.1145/33736426:2(1-29)Online publication date: 4-Feb-2020
  • (2019)Real-Time Trajectory Similarity Processing Using Longest Common Subsequence2019 IEEE 21st International Conference on High Performance Computing and Communications; IEEE 17th International Conference on Smart City; IEEE 5th International Conference on Data Science and Systems (HPCC/SmartCity/DSS)10.1109/HPCC/SmartCity/DSS.2019.00194(1398-1405)Online publication date: Aug-2019
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      cover image ACM Conferences
      CIKM '06: Proceedings of the 15th ACM international conference on Information and knowledge management
      November 2006
      916 pages
      ISBN:1595934332
      DOI:10.1145/1183614
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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      Published: 06 November 2006

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      Author Tags

      1. spatio-temporal similarity search
      2. top-K query processing

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      CIKM06: Conference on Information and Knowledge Management
      November 6 - 11, 2006
      Virginia, Arlington, USA

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      • (2021)i4sea: a big data platform for sea area monitoring and analysis of fishing vessels activityGeo-spatial Information Science10.1080/10095020.2021.197105525:2(132-154)Online publication date: 19-Oct-2021
      • (2020)Distributed Subtrajectory Join on Massive DatasetsACM Transactions on Spatial Algorithms and Systems10.1145/33736426:2(1-29)Online publication date: 4-Feb-2020
      • (2019)Real-Time Trajectory Similarity Processing Using Longest Common Subsequence2019 IEEE 21st International Conference on High Performance Computing and Communications; IEEE 17th International Conference on Smart City; IEEE 5th International Conference on Data Science and Systems (HPCC/SmartCity/DSS)10.1109/HPCC/SmartCity/DSS.2019.00194(1398-1405)Online publication date: Aug-2019
      • (2019)Distributed top-k similarity query on big trajectory streamsFrontiers of Computer Science: Selected Publications from Chinese Universities10.1007/s11704-018-7234-613:3(647-664)Online publication date: 1-Jun-2019
      • (2019)Distributed and parallel processing for real-time and dynamic spatio-temporal graphWorld Wide Web10.1007/s11280-019-00741-6Online publication date: 18-Nov-2019
      • (2018)Spatio-Temporal Data MiningACM Computing Surveys10.1145/316160251:4(1-41)Online publication date: 22-Aug-2018
      • (2018)MDTK: Bandwidth-Saving Framework for Distributed Top-k Similar Trajectory QueryDatabase Systems for Advanced Applications10.1007/978-3-319-91452-7_40(613-629)Online publication date: 13-May-2018
      • (2017)DT-KST: Distributed Top-k Similarity Query on Big Trajectory StreamsDatabase Systems for Advanced Applications10.1007/978-3-319-55753-3_13(199-214)Online publication date: 22-Mar-2017
      • (2016)Top-k Retrieval Techniques in Distributed Sensor SystemsEncyclopedia of GIS10.1007/978-3-319-23519-6_1395-2(1-10)Online publication date: 5-Jul-2016
      • (2015)Adaptive filter updating for energy-efficient top-k queries in wireless sensor networks using gaussian process regressionInternational Journal of Distributed Sensor Networks10.1155/2015/3041982015(140-140)Online publication date: 1-Jan-2015
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