Tinba: Incremental partitioning for efficient trajectory analytics

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dc.authorid0000-0003-1840-9958en_US
dc.contributor.authorTian, Ruijie
dc.contributor.authorZhang, Weishi
dc.contributor.authorWang, Fei
dc.contributor.authorPolat, Kemal
dc.contributor.authorAlenezi, Fayadh
dc.date.accessioned2024-06-05T13:29:56Z
dc.date.available2024-06-05T13:29:56Z
dc.date.issued2023en_US
dc.departmentBAİBÜ, Mühendislik Fakültesi, Elektrik Elektronik Mühendisliği Bölümüen_US
dc.descriptionAcknowledgments This work was supported by the National Key Research and Devel-opment Program of China (2020YFF0410947) and the National Natural Science Foundation of China (62103072) . Additional funding was pro-vided by the China Postdoctoral Science Foundation (2021M690502) .en_US
dc.description.abstractApplications with mobile and sensing devices have already become ubiquitous. In most of these applications, trajectory data is continuously growing to huge volumes. Existing systems for big trajectory data organize trajectories at distributed block storage systems. Systems like DITA that use block storage (e.g., 128 MB each) are more efficient for analytical queries, but they cannot incrementally maintain the partitioned data and do not support delete operations, resulting in difficulties in trajectory analytics. In this paper, we propose an incremental trajectory partitioning framework Tinba that enables distributed block storage systems to efficiently maintain optimized partitions under incremental updates of trajectories. We employ a data flushing technique to bulk ingest trajectory data for random writing in distributed file system (DFS). We recast the incremental partitioning problem as an optimal partitioning problem and prove its NP-hardness. A cost- benefit model is proposed to address the optimal partitioning problem. Moreover, Tinba supports most of the existing similarity measures to quantify the similarity between trajectories. A heuristic is developed to instantiate the Tinba framework. Comprehensive experiments on real-world and synthetic datasets demonstrate the advancements in ingestion performance and partition quality, as opposed to other trajectory partition methods.en_US
dc.description.sponsorshipNational Key Research and Devel-opment Program of China [2020YFF0410947]; National Natural Science Foundation of China [62103072]; China Postdoctoral Science Foundation [2021M690502]en_US
dc.identifier.citationTian, R., Zhang, W., Wang, F., Polat, K., & Alenezi, F. (2023). Tinba: Incremental partitioning for efficient trajectory analytics. Advanced Engineering Informatics, 57, 102064.en_US
dc.identifier.doi10.1016/j.aei.2023.102064
dc.identifier.endpage15en_US
dc.identifier.issn1474-0346
dc.identifier.issn1873-5320
dc.identifier.scopus2-s2.0-85163498752en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage1en_US
dc.identifier.urihttp://dx.doi.org/10.1016/j.aei.2023.102064
dc.identifier.urihttps://hdl.handle.net/20.500.12491/12199
dc.identifier.volume57en_US
dc.identifier.wosWOS:001034139900001en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorPolat, Kemal
dc.language.isoenen_US
dc.publisherElsevier Sci LTDen_US
dc.relation.ispartofAdvanced Engineering Informaticsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectIncremental Partitioningen_US
dc.subjectBig Dataen_US
dc.subjectTrajectory Similarityen_US
dc.subjectDFSen_US
dc.subjectDistributed Computingen_US
dc.subjectNP-Hardnessen_US
dc.titleTinba: Incremental partitioning for efficient trajectory analyticsen_US
dc.typeArticleen_US

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