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

Institutions

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

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

The International Workshop on Spatio-Temporal Data Intelligence and Foundation Models

Yuxuan Liang, Christian S. Jensen, Hao Miao, Yan Zhao et al.
1 citations
Human Mobility and Location-Based Analysis
2.18
article

The International Workshop on Spatio-Temporal Data Intelligence and Foundation Models

Yuxuan Liang, Christian S. Jensen, Hao Miao, Yan Zhao, Bin Yang, Kai Zheng
article en
1 citations

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.

Hong Kong Polytechnic University (HK), University of Electronic Science and Technology of China (CN), East China Normal University (CN), Aalborg University (DK)
Hong Kong Polytechnic University, University of Electronic Science and Technology of China
Openalex Percentile: Top 10%
Human Mobility and Location-Based Analysis
2.18
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.