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Computing longest duration flocks in trajectory data

Published: 10 November 2006 Publication History

Abstract

Moving point object data can be analyzed through the discovery of patterns. We consider the computational efficiency of computing two of the most basic spatio-temporal patterns in trajectories, namely flocks and meetings. The patterns are large enough subgroups of the moving point objects that exhibit similar movement and proximity for a certain amount of time. We consider the problem of computing a longest duration flock or meeting. We give several exact and approximation algorithms, and also show that some variants are as hard as MaxClique to compute and approximate.

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    cover image ACM Conferences
    GIS '06: Proceedings of the 14th annual ACM international symposium on Advances in geographic information systems
    November 2006
    264 pages
    ISBN:1595935290
    DOI:10.1145/1183471
    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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    Publication History

    Published: 10 November 2006

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    Author Tags

    1. approximation algorithms
    2. geometric algorithms
    3. moving objects
    4. spatio-temporal patterns

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    CIKM06: Conference on Information and Knowledge Management
    November 10 - 11, 2006
    Virginia, Arlington, USA

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    Overall Acceptance Rate 257 of 1,238 submissions, 21%

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    Cited By

    View all
    • (2024)Discovering Moving Flock Patterns in Movement Data: A Reeb Graph-Based ApproachApplied Sciences10.3390/app14241188314:24(11883)Online publication date: 19-Dec-2024
    • (2024)Detecting and Visualizing Bond-Forming Convoys in Atomic and Molecular TrajectoriesProceedings of the 32nd ACM International Conference on Advances in Geographic Information Systems10.1145/3678717.3691264(657-660)Online publication date: 29-Oct-2024
    • (2024)SWISP: Distributed Convoy Mining via Sliding Window-based Indexing and Sub-track Partitioning2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00344(4518-4531)Online publication date: 13-May-2024
    • (2024)A framework for spatial-temporal cluster evolution representation and analysis based on graphsScientific Reports10.1038/s41598-024-72504-x14:1Online publication date: 27-Sep-2024
    • (2024)Colossal Trajectory MiningExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.122055238:PDOnline publication date: 15-Mar-2024
    • (2024)Meeting Pattern Detection from Trajectories in Road NetworkWeb and Big Data10.1007/978-981-97-7235-3_27(405-420)Online publication date: 28-Aug-2024
    • (2023)Recognizing Instantaneous Group Patterns in Vessel Trajectory Data: A Snapshot PerspectiveJournal of Marine Science and Engineering10.3390/jmse1112224611:12(2246)Online publication date: 28-Nov-2023
    • (2023)Group Diagrams for simplified representation of scanpathsJournal of Visualization10.1007/s12650-023-00924-426:5(1173-1187)Online publication date: 2-May-2023
    • (2023)SQUID: subtrajectory query in trillion-scale GPS databaseThe VLDB Journal10.1007/s00778-022-00777-732:4(887-904)Online publication date: 19-Jan-2023
    • (2023)Extracting Persistent Clusters in Dynamic Data via Möbius InversionDiscrete & Computational Geometry10.1007/s00454-023-00590-171:4(1276-1342)Online publication date: 11-Oct-2023
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