The International Workshop on Spatio-Temporal Data Intelligence and Foundation Models
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.
Authors
- Yuxuan Liang (ORCID: https://orcid.org/0000-0003-2817-7337)
- Christian S. Jensen (ORCID: https://orcid.org/0000-0002-9697-7670)
- Hao Miao (ORCID: https://orcid.org/0000-0001-9346-7133)
- Yan Zhao (ORCID: https://orcid.org/0000-0002-0242-3707)
- Bin Yang (ORCID: https://orcid.org/0000-0002-1658-1079)
- Kai Zheng (ORCID: https://orcid.org/0000-0002-0217-3998)
Institutions
- Hong Kong Polytechnic University (HK)
- University of Electronic Science and Technology of China (CN)
- East China Normal University (CN)
- Aalborg University (DK)
Publication Details
- Published
- 2025-11-08
- DOI
- https://doi.org/10.1145/3746252.3761601
- Citations
- 1
- Primary Topic
- Human Mobility and Location-Based Analysis
- Type
- article
- Field-Weighted Citation Impact
- 2.18
Funders
- Hong Kong Polytechnic University
- University of Electronic Science and Technology of China