TY - GEN
T1 - Bringing semantics to spatiotemporal data mining
T2 - 33rd IEEE International Conference on Data Engineering, ICDE 2017
AU - Zhang, Chao
AU - Yuan, Quan
AU - Han, Jiawei
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/5/16
Y1 - 2017/5/16
N2 - The pervasiveness of GPS-equipped mobile devices has been nurturing an unprecedented amount of semanticsrich spatiotemporal data. The confluence of spatiotemporal and semantic information offers new opportunities for extracting valuable knowledge about people's behaviors, but meanwhile also introduces its unique challenges that render conventional spatiotemporal data mining techniques inadequate. Consequently, mining semantics-rich spatiotemporal data has attracted significant research attention from the data mining community in the past few years. In this tutorial, we start with reviewing classic spatiotemporal data mining tasks and identifying the new opportunities introduced by semantics-rich spatiotemporal data. Subsequently, we provide a comprehensive introduction of existing techniques for mining semantics-rich spatiotemporal data, covering topics including spatiotemporal activity mining, spatiotemporal event discovery, and spatiotemporal mobility modeling. Finally, we discuss about the limitations of existing research and identify several important future directions.
AB - The pervasiveness of GPS-equipped mobile devices has been nurturing an unprecedented amount of semanticsrich spatiotemporal data. The confluence of spatiotemporal and semantic information offers new opportunities for extracting valuable knowledge about people's behaviors, but meanwhile also introduces its unique challenges that render conventional spatiotemporal data mining techniques inadequate. Consequently, mining semantics-rich spatiotemporal data has attracted significant research attention from the data mining community in the past few years. In this tutorial, we start with reviewing classic spatiotemporal data mining tasks and identifying the new opportunities introduced by semantics-rich spatiotemporal data. Subsequently, we provide a comprehensive introduction of existing techniques for mining semantics-rich spatiotemporal data, covering topics including spatiotemporal activity mining, spatiotemporal event discovery, and spatiotemporal mobility modeling. Finally, we discuss about the limitations of existing research and identify several important future directions.
UR - http://www.scopus.com/inward/record.url?scp=85021220211&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85021220211&partnerID=8YFLogxK
U2 - 10.1109/ICDE.2017.210
DO - 10.1109/ICDE.2017.210
M3 - Conference contribution
AN - SCOPUS:85021220211
T3 - Proceedings - International Conference on Data Engineering
SP - 1455
EP - 1458
BT - Proceedings - 2017 IEEE 33rd International Conference on Data Engineering, ICDE 2017
PB - IEEE Computer Society
Y2 - 19 April 2017 through 22 April 2017
ER -