Large language models (LLMs) are advancing rapidly. Such models have demonstrated strong capabilities in learning from large-scale (unstructured) text data and answering user queries. Users do not need to be experts in structured query languages to interact with systems built upon such models. This provides great opportunities to reduce the barrier of information retrieval for the general public. By introducing LLMs into spatial data management, we envisage an LLM-based spatial database system to learn from both structured and unstructured spatial data. Such a system will offer seamless access to spatial knowledge for the users, thus benefiting individuals, business, and government policy makers alike.
@article{arxiv.2309.17072,
title = {MaaSDB: Spatial Databases in the Era of Large Language Models (Vision Paper)},
author = {Jianzhong Qi and Zuqing Li and Egemen Tanin},
journal= {arXiv preprint arXiv:2309.17072},
year = {2023}
}