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带来的挑战。智能手机、传感器和其他物联网设备在全球城市中的广泛使用，产生了大量的城市时空数据，这些数据往往以高速连续流的形式出现，并带有相当大的噪声和不确定性。这些数据记录了人、车辆等的大量运动信息，并作为各种应用的骨干，如城市交通管理、道路网络规划、基于位置的服务和环境监测。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。** 我们在这项研究中努力回答的一些问题包括：如何有效地处理连续查询（如k最近邻查询和部分路由匹配查询），并在时空流上实时提供答案？如何开发一个嵌入式模型，以综合的方式从个人，空间和时间方面学习人类移动模式？如何构建一个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？** 研究结果将推进时空数据管理和挖掘领域的最新技术水平，提供以分布式方式索引和查询数据的新方法，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实现的各种新应用和服务，为政府、企业和普通民众带来重大利益。

***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.