Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/117006
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dc.contributorDepartment of Computingen_US
dc.creatorMiao, Hen_US
dc.creatorZhao, Yen_US
dc.creatorLiang, Yen_US
dc.creatorYang, Ben_US
dc.creatorZheng, Ken_US
dc.creatorJensen, CSen_US
dc.date.accessioned2026-01-21T08:51:57Z-
dc.date.available2026-01-21T08:51:57Z-
dc.identifier.isbn979-8-4007-2040-6en_US
dc.identifier.urihttp://hdl.handle.net/10397/117006-
dc.descriptionCIKM '25: The 34th ACM International Conference on Information and Knowledge Management, Seoul Republic of Korea, November 10 - 14, 2025en_US
dc.language.isoenen_US
dc.publisherAssociation for Computing Machineryen_US
dc.rights© 2025 Copyright held by the owner/author(s).en_US
dc.rightsThis work is licensed under a Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/).en_US
dc.rightsThe following publication Miao, H., Zhao, Y., Liang, Y., Yang, B., Zheng, K., & Jensen, C. S. (2025, November). The International Workshop on Spatio-Temporal Data Intelligence and Foundation Models. In Proceedings of the 34th ACM International Conference on Information and Knowledge Management (pp. 6920-6922) is available at https://doi.org/10.1145/3746252.3761601.en_US
dc.subjectSpatio-temporal data intelligence,en_US
dc.subjectFoundation modelen_US
dc.titleThe International Workshop on spatio-temporal data intelligence and foundation modelsen_US
dc.typeConference Paperen_US
dc.identifier.spage6920en_US
dc.identifier.epage6922en_US
dc.identifier.doi10.1145/3746252.3761601en_US
dcterms.abstractSpatio-temporal data intelligence, which includes sensing, managing, and mining large-scale data across space and time, plays a pivotal role in understanding complex systems in real-world applications, such as urban computing and smart cities. With the rapid evolution of foundation models and their growing potential to transform spatio-temporal analytics, we propose a comprehensive half-day workshop (with at least 5 accepted papers, 3 keynote talks, 1 panel discussion, and over 50 attendees) at CIKM 2025, catering to professionals, researchers, and practitioners who are interested in spatio-temporal data intelligence and foundation models to address real-world challenges. The workshop will not only offer a platform for knowledge exchange but also acknowledge outstanding contributions through a distinguished Best Paper Award. A dedicated panel discussion will explore recent advances, emerging trends, and open challenges in integrating spatio-temporal data and emerging machine learning techniques, fostering dialogue between academia and industry. Note that this will be the eleventh time that our core members have organized a similar workshop. The previous 10 workshops were hosted in top-tier data mining and management venues, e.g., SIGKDD, WWW, and IJCAI, each of which attracted over 60 participants and 25 submissions on average.en_US
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationIn CIKM '25: Proceedings of the 34th ACM International Conference on Information and Knowledge Management, p. 6920-6922. New York, NY, USA: Association for Computing Machinery, 2022en_US
dcterms.issued2025-
dc.relation.conferenceACM International Conference on Information & Knowledge Management [CIKM]en_US
dc.publisher.placeNew Yorken_US
dc.description.validate202601 bcwhen_US
dc.description.oaVersion of Recorden_US
dc.identifier.FolderNumberOA_Others-
dc.description.fundingSourceOthersen_US
dc.description.fundingTextThis work is partially supported by the Hong Kong Polytechnic University project (No. P0058185) and DFF Inge Lehmann grant (No. 4303-00014).en_US
dc.description.pubStatusPublisheden_US
dc.description.oaCategoryCCen_US
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