Managing and Mining Urban Spatio-Temporal Data

管理和挖掘城市时空数据

• 批准号: RGPIN-2017-05723
• 负责人: Yu, Xiaohui
• 金额: $ 1.68万
• 依托单位: York University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=711779
• 关键词: Managing; Mining; Urban; Spatio; Temporal

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.

提出的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世界各地城市的广泛使用，产生了大量的城市时空数据，这些数据往往呈现为高速连续流，具有相当大的噪音和不确定性。这些数据记录了大量的人、车等的运动信息，是城市交通管理、路网规划、定位服务、环境监测等多种应用的骨干。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。