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ECMA: An Efficient Convoy Mining Algorithm for Moving Objects

Published: 30 October 2021 Publication History

Abstract

With the popularity of mobile devices equipped with positioning devices, it is convenient to obtain enormous amounts of trajectory data. The development promotes the study of extracting moving patterns from trajectory data of moving objects. One such pattern is the convoy, which refers to a group of objects moving together for a period of time. The existing convoy mining algorithms have a large time cost because they adopt a density-based clustering algorithm over global objects. In this paper, we propose an efficient convoy mining algorithm (ECMA) that adopts the divide-and-conquer methodology. A block-based partition model (BP-Model) is designed to divide objects into multiple maximized connected nonempty block areas (MOBAs). The convoy mining problem is then solved by processing each MOBA sequentially, which significantly reduces the time cost of convoy mining. In the experiments, we evaluate the performance of our algorithm on real-world datasets. The results show that the ECMA is more efficient than existing convoy mining algorithms.

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    cover image ACM Conferences
    CIKM '21: Proceedings of the 30th ACM International Conference on Information & Knowledge Management
    October 2021
    4966 pages
    ISBN:9781450384469
    DOI:10.1145/3459637
    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: 30 October 2021

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    Author Tags

    1. convoy pattern mining
    2. geospatial data
    3. moving objects

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    • (2024)Debiasing the Conversion Rate Prediction Model in the Presence of Delayed Implicit FeedbackEntropy10.3390/e2609079226:9(792)Online publication date: 15-Sep-2024
    • (2024)Colossal Trajectory MiningExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.122055238:PDOnline publication date: 27-Feb-2024
    • (2024)ECEQ: efficient multi-source contact event query processing for moving objectsWorld Wide Web10.1007/s11280-024-01309-927:6Online publication date: 30-Sep-2024
    • (2024)EPCQ: Efficient Privacy-Preserving Contact Query Processing over Trajectory Data in CloudWeb and Big Data10.1007/978-981-97-7241-4_12(183-198)Online publication date: 28-Aug-2024
    • (2023)Co-Movement Pattern Mining from VideosProceedings of the VLDB Endowment10.14778/3632093.363211917:3(604-616)Online publication date: 1-Nov-2023
    • (2023)Efficient Multi-source Contact Event Query Processing for Moving Objects2023 IEEE International Conference on Data Mining (ICDM)10.1109/ICDM58522.2023.00132(1109-1114)Online publication date: 1-Dec-2023
    • (2023)Automated Detection of Trajectory Groups Based on SNN-Clustering and Relevant Frequent Itemsets2023 IEEE 10th International Conference on Data Science and Advanced Analytics (DSAA)10.1109/DSAA60987.2023.10302564(1-10)Online publication date: 9-Oct-2023

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