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.
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.
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.
