Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/117006
| DC Field | Value | Language |
|---|---|---|
| dc.contributor | Department of Computing | en_US |
| dc.creator | Miao, H | en_US |
| dc.creator | Zhao, Y | en_US |
| dc.creator | Liang, Y | en_US |
| dc.creator | Yang, B | en_US |
| dc.creator | Zheng, K | en_US |
| dc.creator | Jensen, CS | en_US |
| dc.date.accessioned | 2026-01-21T08:51:57Z | - |
| dc.date.available | 2026-01-21T08:51:57Z | - |
| dc.identifier.isbn | 979-8-4007-2040-6 | en_US |
| dc.identifier.uri | http://hdl.handle.net/10397/117006 | - |
| dc.description | CIKM '25: The 34th ACM International Conference on Information and Knowledge Management, Seoul Republic of Korea, November 10 - 14, 2025 | en_US |
| dc.language.iso | en | en_US |
| dc.publisher | Association for Computing Machinery | en_US |
| dc.rights | © 2025 Copyright held by the owner/author(s). | en_US |
| dc.rights | This work is licensed under a Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/). | en_US |
| dc.rights | The 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.subject | Spatio-temporal data intelligence, | en_US |
| dc.subject | Foundation model | en_US |
| dc.title | The International Workshop on spatio-temporal data intelligence and foundation models | en_US |
| dc.type | Conference Paper | en_US |
| dc.identifier.spage | 6920 | en_US |
| dc.identifier.epage | 6922 | en_US |
| dc.identifier.doi | 10.1145/3746252.3761601 | en_US |
| dcterms.abstract | Spatio-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.accessRights | open access | en_US |
| dcterms.bibliographicCitation | In 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, 2022 | en_US |
| dcterms.issued | 2025 | - |
| dc.relation.conference | ACM International Conference on Information & Knowledge Management [CIKM] | en_US |
| dc.publisher.place | New York | en_US |
| dc.description.validate | 202601 bcwh | en_US |
| dc.description.oa | Version of Record | en_US |
| dc.identifier.FolderNumber | OA_Others | - |
| dc.description.fundingSource | Others | en_US |
| dc.description.fundingText | This 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.pubStatus | Published | en_US |
| dc.description.oaCategory | CC | en_US |
| Appears in Collections: | Conference Paper | |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| 3746252.3761601.pdf | 509.45 kB | Adobe PDF | View/Open |
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