Foundation Models for Spatio-Temporal Data Mining and
Management
Organizers: Haomin Wen, Hua Wei, Yuxuan Liang, Huaiyu Wan, and Roger
Zimmermann
Abstract
In recent years, foundation models (FMs) have significantly enhanced various tasks and
transformed model design in time series analysis. In this manuscript, We propose a
comprehensive 3 hours tutorial at MDM 2025, tailored for professionals, researchers, and
practitioners interested in utilizing FMs for spatio-temporal data mining and
management. This tutorial offers insights into FM theory, implementation, and practical
applications, including principles, pre-processing techniques, and modeling strategies.
Attendees will also learn best practices for integrating FMs into workflows and
exploring applications in diverse fields, such as finance, healthcare, and
transportation. The tutorial promises enhanced understanding, practical skills
acquisition, and networking opportunities, bridging the gap between theory and practice.
Large Language Models for Urban Mobility
Organizers: Youssef Hussein, Mohamed Hemdan, and Mohamed F. Mokbel
Abstract
This Advanced Seminar provides a comprehensive overview of the research landscape of
employing Large Language Models (LLMs) for Urban Mobility applications. The presented
work in this seminar is categorized based on how LLMs are employed to serve various
urban mobility applications. This goes from employing LLMs as a black box with a bit of
prompt engineering, to fine-tuning LLMs to fit urban mobility applications, to
completely retrain a vanilla LLM architecture with urban mobility data, to modifying the
internal LLM loss function to fit urban mobility applications. The seminar concludes by
presenting a set of benchmarking and evaluation work while pointing out to research
gaps, open problems, and future research directions for employing LLMs to urban mobility
applications.
A Brief Introduction to Geospatial Big Data Analytics with
Apache AsterixDB
Organizers: Suryaa Charan Shivakumar, Ian Maxon, Ahmed Eldawy, Michael Carey and
Vassilis Tsotras
Abstract
The potential of geospatial data is vast, and its value increases when combined with
temporal, textual, or other nonspatial features. However, managing and analyzing
geospatial data at scale is inherently challenging due to the computational and storage
requirements, especially when additional optimization is required for combined features.
While there are numerous solutions for big spatial data management, many struggle to
support non-spatial operations effectively, with limited options in the open-source
space that excel at handling both spatial and non-spatial queries comprehensively. This
seminar explores scalable geospatial data management and analytics, focusing on
approaches and techniques that address these challenges. Participants will gain hands-on
experience in processing complex queries involving spatial, temporal, and textual
features using a real-world Big Data Management System. Through practical examples and
exercises, attendees will learn how to tackle the complexities of scalable geospatial
analytics in modern data systems.
