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Title: The International Workshop on spatio-temporal data intelligence and foundation models
Authors: Miao, H 
Zhao, Y
Liang, Y
Yang, B
Zheng, K
Jensen, CS
Issue Date: 2025
Source: 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
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.
Keywords: Spatio-temporal data intelligence,
Foundation model
Publisher: Association for Computing Machinery
ISBN: 979-8-4007-2040-6
DOI: 10.1145/3746252.3761601
Description: CIKM '25: The 34th ACM International Conference on Information and Knowledge Management, Seoul Republic of Korea, November 10 - 14, 2025
Rights: © 2025 Copyright held by the owner/author(s).
This work is licensed under a Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/).
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.
Appears in Collections:Conference Paper

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