Dynamic trajectory partition optimization method based on historical trajectory data

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dc.authorid0000-0001-8913-9057
dc.authorid0000-0002-7201-6963
dc.authorid0000-0003-1840-9958
dc.contributor.authorYu, Xiang
dc.contributor.authorZhai, Huawei
dc.contributor.authorTian, Ruijie
dc.contributor.authorGuan, Yao
dc.contributor.authorPolat, Kemal
dc.contributor.authorAlhudhaif, Adi
dc.date.accessioned2024-09-25T19:59:49Z
dc.date.available2024-09-25T19:59:49Z
dc.date.issued2024
dc.departmentBAİBÜ, Mühendislik Fakültesi, Elektrik Elektronik Mühendisliği Bölümü en_US
dc.description.abstractPartitioning dynamic trajectory data can improve the efficiency and accuracy of trajectory data processing, provide a foundation for trajectory data mining and analysis. However, with the continuous growth of trajectory data scales and the urgent demand for trajectory query efficiency and accuracy, partitioning methods have become crucial. The partitioning method of dynamic trajectory data faces significant challenges in terms of spatiotemporal trajectory locality, partition load balancing, and partition time. To address these challenges, we propose a method based on historical trajectory pre-partitioning, which can store data more effectively in distributed systems. We partition similar historical trajectory data to achieve preliminary partitioning of the data. In addition, we also construct a cost model to ensure that the workload of each partition is close to consistency. Extensive experiments have demonstrated the excellent partitioning efficiency and query efficiency achieved by our design compared to other partitioning methods.en_US
dc.description.sponsorshipLiaoning Provincial Natural Science Foundation of China [2022-MS-420]en_US
dc.description.sponsorshipAcknowledgment This work was supported by the Liaoning Provincial Natural Science Foundation of China (Grant No. 2022-MS-420) .en_US
dc.identifier.doi10.1016/j.asoc.2023.111120
dc.identifier.issn1568-4946
dc.identifier.issn1872-9681
dc.identifier.scopus2-s2.0-85179476681en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1016/j.asoc.2023.111120
dc.identifier.urihttps://hdl.handle.net/20.500.12491/13939
dc.identifier.volume151en_US
dc.identifier.wosWOS:001135071700001en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorPolat, Kemal
dc.institutionauthorid0000-0003-1840-9958
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.relation.ispartofApplied Soft Computingen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmzYK_20240925en_US
dc.subjectPre-partitioningen_US
dc.subjectDynamic trajectory partitioningen_US
dc.subjectSimilarity functionen_US
dc.subjectHistorical Trajectory
dc.subjectQuery Efficiency and Accuracy
dc.subjectUrgent Demand
dc.titleDynamic trajectory partition optimization method based on historical trajectory dataen_US
dc.typeArticleen_US

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