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: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=634798
• 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.

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。