Managing and Mining Urban Spatio-Temporal Data

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***The unifying theme of the proposed research is to address the challenges arising from managing and mining urban spatio-temporal data. The wide-spread use of smart phones, sensors and other IoT devices in cities world-wide has given rise to a huge volume of urban spatio-temporal data, which often present themselves as high-velocity continuous streams with considerable noise and uncertainties. These data record a vast amount of movement information of people, vehicles, etc., and serve as the backbone of a variety of applications, such as urban traffic management, road network planning, location-based services, and environmental monitoring. While governments, businesses and other organizations have realized the tremendous value of urban spatio-temporal data, how to effectively tap into this potential is still an elusive goal. ******Some of the questions we strive to answer in this research include: How to efficiently process continuous queries (such as k nearest-neighbor queries and partial route matching queries) and provide answers in real-time over spatio-temporal streams? How do develop an embedding model to learn human mobility patterns from personal, spatial and temporal aspects in an integrated manner? How to construct a probabilistic model to capture the underlying intention of movement so that we could have a deeper insight into the human mobility patterns? ******The results will advance the state of the art in the field of spatio-temporal data management and mining, providing new ways of indexing and querying data in a distributed fashion as well as offering novel probabilistic models for understanding human mobility patterns. This project has the potential to bring significant benefit to governments, businesses and ordinary people, through a variety of new applications and services enabled by the research results.