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Querying Recurrent Convoys over Trajectory Data

Authors:

Munkh-Erdene Yadamjav,

Farhana M. Choudhury,

Hanan SametAuthors Info & Claims

ACM Transactions on Intelligent Systems and Technology (TIST), Volume 11, Issue 5

Article No.: 59, Pages 1 - 24

https://doi.org/10.1145/3400730

Published: 03 August 2020 Publication History

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Abstract

Moving objects equipped with location-positioning devices continuously generate a large amount of spatio-temporal trajectory data. An interesting finding over a trajectory stream is a group of objects that are travelling together for a certain period of time. We observe that existing studies on mining co-moving objects do not consider an important correlation between co-moving objects, which is the reoccurrence of the co-moving pattern. In this study, we propose the problem of finding recurrent co-moving patterns from streaming trajectories, enabling us to discover recent co-moving patterns that are repeated within a given time period. Experimental results on real-life trajectory data verify the efficiency and effectiveness of our method.

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Cited By

Kwon YMoon JChung Y(2024)Noise-Tolerant Trajectory Distance Computation in the Presence of Inherent Noise for Video Surveillance ApplicationsIEEE Access10.1109/ACCESS.2024.342209812(92400-92418)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3422098
Zhou CLiu GHuang LWen Y(2023)Spatiotemporal Companion Pattern (STCP) Mining of Ships Based on Trajectory FeaturesJournal of Marine Science and Engineering10.3390/jmse1103052811:3(528)Online publication date: 28-Feb-2023
https://doi.org/10.3390/jmse11030528
Zhang DMa THu JBei YTan KChen G(2023)Co-Movement Pattern Mining from VideosProceedings of the VLDB Endowment10.14778/3632093.363211917:3(604-616)Online publication date: 1-Nov-2023
https://dl.acm.org/doi/10.14778/3632093.3632119
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Index Terms

Querying Recurrent Convoys over Trajectory Data
1. Information systems
  1. Information systems applications
    1. Data mining
      1. Data stream mining
2. Theory of computation
  1. Theory and algorithms for application domains
    1. Database theory
      1. Data structures and algorithms for data management

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Published In

cover image ACM Transactions on Intelligent Systems and Technology

ACM Transactions on Intelligent Systems and Technology Volume 11, Issue 5

Survey Paper and Regular Paper

October 2020

325 pages

ISSN:2157-6904

EISSN:2157-6912

DOI:10.1145/3409643

Editor:
Yu Zheng
JD Digits, China

Issue’s Table of Contents

Copyright © 2020 ACM.

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 the author(s) 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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 03 August 2020

Accepted: 01 May 2020

Revised: 01 April 2020

Received: 01 December 2019

Published in TIST Volume 11, Issue 5

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Cited By

Kwon YMoon JChung Y(2024)Noise-Tolerant Trajectory Distance Computation in the Presence of Inherent Noise for Video Surveillance ApplicationsIEEE Access10.1109/ACCESS.2024.342209812(92400-92418)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3422098
Zhou CLiu GHuang LWen Y(2023)Spatiotemporal Companion Pattern (STCP) Mining of Ships Based on Trajectory FeaturesJournal of Marine Science and Engineering10.3390/jmse1103052811:3(528)Online publication date: 28-Feb-2023
https://doi.org/10.3390/jmse11030528
Zhang DMa THu JBei YTan KChen G(2023)Co-Movement Pattern Mining from VideosProceedings of the VLDB Endowment10.14778/3632093.363211917:3(604-616)Online publication date: 1-Nov-2023
https://dl.acm.org/doi/10.14778/3632093.3632119
Liu YDai HLi JChen YYang GWang J(2023)BP-Model-based convoy mining algorithms for moving objectsExpert Systems with Applications10.1016/j.eswa.2022.118860213(118860)Online publication date: Mar-2023
https://doi.org/10.1016/j.eswa.2022.118860
Trisminingsih RShaikh SAmagasa TKitagawa HMatono A(2023)TraPM: A Framework for Online Pattern Matching Over Trajectory StreamsInformation Integration and Web Intelligence10.1007/978-3-031-48316-5_45(510-525)Online publication date: 4-Dec-2023
https://dl.acm.org/doi/10.1007/978-3-031-48316-5_45
Zheng ZLi ZLi JJiang HLi TGuo B(2022)Utility-aware and Privacy-preserving Trajectory Synthesis Model that Resists Social Relationship Privacy AttacksACM Transactions on Intelligent Systems and Technology10.1145/349516013:3(1-28)Online publication date: 11-May-2022
https://dl.acm.org/doi/10.1145/3495160
Tedjopurnomo DLi XBao ZCong GChoudhury FQin A(2021)Similar Trajectory Search with Spatio-Temporal Deep Representation LearningACM Transactions on Intelligent Systems and Technology10.1145/346668712:6(1-26)Online publication date: 11-Dec-2021
https://dl.acm.org/doi/10.1145/3466687
Löffler CReeb LDzibela DMarzilger RWitt NEskofier BMutschler C(2021)Deep Siamese Metric Learning: A Highly Scalable Approach to Searching Unordered Sets of TrajectoriesACM Transactions on Intelligent Systems and Technology10.1145/346505713:1(1-23)Online publication date: 29-Nov-2021
https://dl.acm.org/doi/10.1145/3465057
Liu YDai HLi BLi JYang GWang JDemartini GZuccon GCulpepper JHuang ZTong H(2021)ECMAProceedings of the 30th ACM International Conference on Information & Knowledge Management10.1145/3459637.3482255(1089-1098)Online publication date: 26-Oct-2021
https://dl.acm.org/doi/10.1145/3459637.3482255

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