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Project Summary:

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.

The unifying theme of the project is to address the challenges arising from managing and mining urban spatio-temporal data. Some of the questions we strive to answer are: How to improve the quality of such data to provide a reliable basis for data analytics? How to efficiently process continuous queries (such as k nearest-neighbor queries) and discover patterns over spatio-temporal streams? How to construct a probabilistic model to capture the underlying intention of movement? How to use this model to support advanced applications, such as traffic flow forecasting, dynamic navigation, and next location prediction?

Novel models and methods developed from this project will help lay the data management and analytics foundation for a wide spectrum of applications, and provide a better understanding of human mobility patterns.

Project Description:

Project Type:
Funded

Project Role:
Principal Investigator

Country 1:
Canada

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Funder:
NSERC

Year Project Started:
2017

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