@article{Chen_Shang_2019, title={Region-Based Message Exploration over Spatio-Temporal Data Streams}, volume={33}, url={https://ojs.aaai.org/index.php/AAAI/article/view/3875}, DOI={10.1609/aaai.v33i01.3301873}, abstractNote={<p>Massive amount of spatio-temporal data that contain location and text content are being generated by location-based social media. These spatio-temporal messages cover a wide range of topics. It is of great significance to discover local trending topics based on users’ location-based and topicbased requirements. We develop a region-based message exploration mechanism that retrieve spatio-temporal message clusters from a stream of spatio-temporal messages based on users’ preferences on message topic and message spatial distribution. Additionally, we propose a region summarization algorithm that finds a subset of representative messages in a cluster to summarize the topics and the spatial attributes of messages in the cluster. We evaluate the efficacy and efficiency of our proposal on two real-world datasets and the results demonstrate that our solution is capable of high efficiency and effectiveness compared with baselines.</p>}, number={01}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Chen, Lisi and Shang, Shuo}, year={2019}, month={Jul.}, pages={873-880} }