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.

Though polar scientists entertain having huge amounts of publicly available datasets, they face the challenge that working with such data is a cumbersome process that requires downloading tons of unnecessary data and writing various scripts on top of it. This hinders their ability to perform any kind of interactive analysis. This paper presents Polaris; a novel open-source system infrastructure for Polar science that is highly Interactive and Scalable. Polaris is designed based on three observations that distinguish the query workload of polar scientists, namely, all queries are spatio-temporal, not all data are equal, and the large majority of queries are aggregates. Polaris is equipped with a hierarchical spatio-temporal index structure that stores precomputed aggregates for data of interest. Experimental results with a real Polaris prototype and real scientific data show that it achieves highly interactive and scalable data access, enabling interactive analysis of polar science data.

This demonstration presents Polaris; a novel open-source system infrastructure for Polar science that is highly Interactive and Scalable. Polaris is designed based on three observations that distinguish the query workload of polar scientists, namely, all queries are spatio-temporal, not all data are equal, and the large majority of queries are aggregates. With this, Polaris is equipped with a hierarchical spatio-temporal index structure that stores precomputed aggregates for data of interest. Audience will be able to experience Polaris through various scenarios that show the interactivity and scalability as well as Polaris optimized query processes.

This tutorial provides a comprehensive overview of the research landscape of employing Large Language Models (LLMs) to spatial analysis queries. The tutorial categorizes the research in this area based on how LLMs are employed to serve such queries. This goes from employing LLMs as is, to fine-tuning LLMs, to completely retrain LLM architectures, to modifying the LLM internals to fit spatial queries. The tutorial concludes by a set of benchmarks and pointing out to research gaps and future research directions.