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description Publicationkeyboard_double_arrow_right Article , External research report 2022 ItalyPublisher:Institute of Electrical and Electronics Engineers (IEEE) Funded by:ARC | Discovery Projects - Gran..., ARC | Linkage Projects - Grant ...ARC| Discovery Projects - Grant ID: DP200103650 ,ARC| Linkage Projects - Grant ID: LP180100018Fengmei Jin; Wen Hua; Thomas Zhou; Jiajie Xu; Matteo Francia; Maria E. Orlowska; Xiaofang Zhou;Trajectory-based spatiotemporal entity linking is to match the same moving object in different datasets based on their movement traces. It is a fundamental step to support spatiotemporal data integration and analysis. In this paper, we study the problem of spatiotemporal entity linking using effective and concise signatures extracted from their trajectories. This linking problem is formalized as a k-nearest neighbor (k-NN) query on the signatures. Four representation strategies (sequential, temporal, spatial, and spatiotemporal) and two quantitative criteria (commonality and unicity) are investigated for signature construction. A simple yet effective dimension reduction strategy is developed together with a novel indexing structure called the WR-tree to speed up the search. A number of optimization methods are proposed to improve the accuracy and robustness of the linking. Our extensive experiments on real-world datasets verify the superiority of our approach over the state-of-the-art solutions in terms of both accuracy and efficiency. Comment: 15 pages, 3 figures, 15 tables
Archivio istituziona... arrow_drop_down arXiv.org e-Print ArchiveOther literature type . Preprint . 2020Data sources: arXiv.org e-Print ArchiveIEEE Transactions on Knowledge and Data EngineeringArticle . 2022 . Peer-reviewedLicense: IEEE CopyrightData sources: Crossrefhttps://doi.org/10.48550/arxiv...Article . 2020License: arXiv Non-Exclusive DistributionData sources: Dataciteadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1109/tkde.2020.3036633&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen bronze 4 citations 4 popularity Top 10% influence Average impulse Average Powered by BIP!more_vert Archivio istituziona... arrow_drop_down arXiv.org e-Print ArchiveOther literature type . Preprint . 2020Data sources: arXiv.org e-Print ArchiveIEEE Transactions on Knowledge and Data EngineeringArticle . 2022 . Peer-reviewedLicense: IEEE CopyrightData sources: Crossrefhttps://doi.org/10.48550/arxiv...Article . 2020License: arXiv Non-Exclusive DistributionData sources: Dataciteadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1109/tkde.2020.3036633&type=result"></script>'); --> </script>
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description Publicationkeyboard_double_arrow_right Article , External research report 2022 ItalyPublisher:Institute of Electrical and Electronics Engineers (IEEE) Funded by:ARC | Discovery Projects - Gran..., ARC | Linkage Projects - Grant ...ARC| Discovery Projects - Grant ID: DP200103650 ,ARC| Linkage Projects - Grant ID: LP180100018Fengmei Jin; Wen Hua; Thomas Zhou; Jiajie Xu; Matteo Francia; Maria E. Orlowska; Xiaofang Zhou;Trajectory-based spatiotemporal entity linking is to match the same moving object in different datasets based on their movement traces. It is a fundamental step to support spatiotemporal data integration and analysis. In this paper, we study the problem of spatiotemporal entity linking using effective and concise signatures extracted from their trajectories. This linking problem is formalized as a k-nearest neighbor (k-NN) query on the signatures. Four representation strategies (sequential, temporal, spatial, and spatiotemporal) and two quantitative criteria (commonality and unicity) are investigated for signature construction. A simple yet effective dimension reduction strategy is developed together with a novel indexing structure called the WR-tree to speed up the search. A number of optimization methods are proposed to improve the accuracy and robustness of the linking. Our extensive experiments on real-world datasets verify the superiority of our approach over the state-of-the-art solutions in terms of both accuracy and efficiency. Comment: 15 pages, 3 figures, 15 tables
Archivio istituziona... arrow_drop_down arXiv.org e-Print ArchiveOther literature type . Preprint . 2020Data sources: arXiv.org e-Print ArchiveIEEE Transactions on Knowledge and Data EngineeringArticle . 2022 . Peer-reviewedLicense: IEEE CopyrightData sources: Crossrefhttps://doi.org/10.48550/arxiv...Article . 2020License: arXiv Non-Exclusive DistributionData sources: Dataciteadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1109/tkde.2020.3036633&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen bronze 4 citations 4 popularity Top 10% influence Average impulse Average Powered by BIP!more_vert Archivio istituziona... arrow_drop_down arXiv.org e-Print ArchiveOther literature type . Preprint . 2020Data sources: arXiv.org e-Print ArchiveIEEE Transactions on Knowledge and Data EngineeringArticle . 2022 . Peer-reviewedLicense: IEEE CopyrightData sources: Crossrefhttps://doi.org/10.48550/arxiv...Article . 2020License: arXiv Non-Exclusive DistributionData sources: Dataciteadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1109/tkde.2020.3036633&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu