Reference number : GC-2017-Q1-02828, amendment number : , agreement type : G, recipient legal name : Yu, Xiaohui (York University)|Yu, Xiaohui (Université York), recipient country : CA, recipient province : nan, recipient city : Ontario, recipient postal code : nan, program name english : Discovery Grants Program - Individual, program name french : Programme de subventions à la découverte - individuelles, program purpose english : 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., program purpose french : 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., agreement title english : Managing and Mining Urban Spatio-Temporal Data, agreement title french : Managing and Mining Urban Spatio-Temporal Data, agreement number : RGPIN, agreement value : 115000.0, agreement start date : 2017-05-10, agreement end date : nan, description english : nan, description french : nan, expected results english : nan, expected results french : nan, additional information english : Grant or Award spanning more than one fiscal year. (2017-2018 to 2022-2023), additional information french : Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier. (2017-2018 à 2022-2023), owner organization : nserc-crsng, owner organization title : Natural Sciences and Engineering Research Council of Canada | Conseil de recherches en sciences naturelles et en génie du Canada