Managing and Mining Urban Spatio-Temporal Data

*拟议研究的统一主题是解决管理和挖掘城市时空数据所产生的挑战。智能手机、传感器和其他物联网设备在世界各地城市的广泛使用产生了海量的城市时空数据，这些数据往往呈现为具有相当大的噪声和不确定性的高速连续流。这些数据记录了大量的人、车辆等运动信息，是城市交通管理、路网规划、基于位置的服务和环境监测等各种应用的基础。虽然政府、企业和其他组织已经意识到城市时空数据的巨大价值，但如何有效地挖掘这一潜力仍然是一个难以实现的目标。*本研究致力于回答的一些问题包括：如何高效地处理连续查询(如k近邻查询和部分路径匹配查询)，并在时空流上实时提供答案？如何开发一个嵌入模型，以综合的方式从个人、空间和时间方面学习人类的流动模式？如何构建概率模型来捕捉运动的潜在意图，以便我们能够更深入地了解人类的移动模式？*该结果将推动时空数据管理和挖掘领域的发展，为以分布式方式索引和查询数据提供新的方法，并为理解人类移动模式提供新的概率模型。该项目有可能通过研究成果带来的各种新的应用和服务，为政府、企业和普通民众带来显著的好处。

***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.