RTMatch: Real-time location prediction based on trajectory pattern matching

Dong Zhenjiang, Deng Jia, Jiang Xiaohui, Wang Yongli

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

Abstract

Due to the universality of mobile devices, such as GPS and the devices of location-based services, there is a growing number of mobile trajectory data. This provides the opportunities for innovation of analyzing trajectory and extracting information. We proposed a new method to predict next location of moving object - RTMatch. The main idea of the method is to store and query the trajectory frequency pattern (named T-pattern) of moving objects by designing a data structure - RTPT (Real Time Pattern Tree) and HT (Hash Table) contains the spatio-temporal information, and then find a best matched path on the tree (the best T-pattern matches the trajectory to be predicted). The RTMatch can provide real-time analysis during the on the fly processing. Experiments on the actual data prove that our method is more accuracy and efficiency than some existing methods.

Original languageEnglish (US)
Title of host publicationDatabase Systems for Advanced Applications - DASFAA 2017 International Workshops
Subtitle of host publicationBDMS, BDQM, SeCoP, and DMMOOC, Proceedings
EditorsLijun Chang, Goce Trajcevski, Wen Hua, Zhifeng Bao
PublisherSpringer Verlag
Pages103-117
Number of pages15
ISBN (Print)9783319557045
DOIs
StatePublished - Jan 1 2017
Externally publishedYes
EventInternational Workshops on Database Systems for Advanced Applications, DASFAA 2017, 4th International Workshop on Big Data Management and Service, BDMS 2017, 2nd Workshop on Big Data Quality Management, BDQM 2017, 4th International Workshop on Semantic Computing and Personalization, SeCoP 2017, 1st International Workshop on Data Management and Mining on MOOCs, DMMOOC 2017 - Suzhou, China
Duration: Mar 27 2017Mar 30 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10179 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceInternational Workshops on Database Systems for Advanced Applications, DASFAA 2017, 4th International Workshop on Big Data Management and Service, BDMS 2017, 2nd Workshop on Big Data Quality Management, BDQM 2017, 4th International Workshop on Semantic Computing and Personalization, SeCoP 2017, 1st International Workshop on Data Management and Mining on MOOCs, DMMOOC 2017
CountryChina
CitySuzhou
Period3/27/173/30/17

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Keywords

  • Location prediction
  • Real time
  • Spatio-temporal data mining
  • Trajectory pattern

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  • Cite this

    Zhenjiang, D., Jia, D., Xiaohui, J., & Yongli, W. (2017). RTMatch: Real-time location prediction based on trajectory pattern matching. In L. Chang, G. Trajcevski, W. Hua, & Z. Bao (Eds.), Database Systems for Advanced Applications - DASFAA 2017 International Workshops: BDMS, BDQM, SeCoP, and DMMOOC, Proceedings (pp. 103-117). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10179 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-55705-2_8