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.
About the speaker:
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 60 papers in premier conferences and journals of related areas such as KDD, TKDE, ICDM, SDM, AAAI, IJCAI, CIKM, TOIS, KAIS, etc.