Region-Based Message Exploration over Spatio-Temporal Data Streams


  • Lisi Chen Hong Kong Baptist University
  • Shuo Shang King Abdullah University of Science and Technology



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.




How to Cite

Chen, L., & Shang, S. (2019). Region-Based Message Exploration over Spatio-Temporal Data Streams. Proceedings of the AAAI Conference on Artificial Intelligence, 33(01), 873-880.



AAAI Technical Track: Applications