时间：2019年11月8日 星期五 下午2：30
With the fast development of various positioning techniques such as Global Position System (GPS), mobile devices and remote sensing, spatio-temporal data generated in urban areas has become increasingly available nowadays. Mining valuable knowledge from spatio-temporal data is critically important for better design of smart city applications, such as human mobility monitoring, smart transportation, urban planning, public safety, and environmental management. As the number, volume and resolution of spatio-temporal datasets increase rapidly, traditional data mining methods, especially statistics-based methods for dealing with such data are becoming overwhelmed. Recently, with the great success of AI techniques, especially deep learning methods, various AI techniques have been widely applied in analyzing rich spatio-temporal data generated in urban areas. In this tutorial, I will present our recent research on AI empowered spatio-temporal data mining. First, I will introduce the new challenges and opportunities of applying AI techniques to address various spatio-temporal data mining tasks in general. Then I will summarize a general framework to show the pipeline of AI empowered spatio-temporal data mining, followed by several examples illustrating how AI techniques including both shallow models and deep learning models can be utilized in the applications of demand-supply prediction in on-demand services, urban traffic prediction and urban crowd flow prediction. Finally, I will conclude the limitations of current research and point out future research directions.
Dr. Senzhang Wang is currently an associate professor at College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, and also a “Hong Kong Scholar” Postdoc Fellow at Department of Computing, The Hong Kong Polytechnic University. His main research focus is on spatio-temporal data mining, social computing, graph mining and smart city. He has published more than 70 papers in premier conferences and journals of related areas such as KDD, TKDE, ICDM, SDM, AAAI, IJCAI, CIKM, TOIS, KAIS, etc.
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