Making sense of spatial trajectories

Zhou, Xiaofang, Zheng, Kai, Jueng, Hoyoung, Xu, Jiajie and Sadiq, Shazia (2015). Making sense of spatial trajectories. In: CIKM 2015 - Proceedings of the 24th ACM International Conference on Information and Knowledge Management. 24th ACM International Conference on Information and Knowledge Management, CIKM 2015, Melbourne, Australia, (671-672). 19-23 October 2015. doi:10.1145/2806416.2806418


Author Zhou, Xiaofang
Zheng, Kai
Jueng, Hoyoung
Xu, Jiajie
Sadiq, Shazia
Title of paper Making sense of spatial trajectories
Conference name 24th ACM International Conference on Information and Knowledge Management, CIKM 2015
Conference location Melbourne, Australia
Conference dates 19-23 October 2015
Convener Bailey, James
Proceedings title CIKM 2015 - Proceedings of the 24th ACM International Conference on Information and Knowledge Management
Journal name International Conference on Information and Knowledge Management, Proceedings
Series International Conference on Information and Knowledge Management, Proceedings
Place of Publication New York, NY United States
Publisher The Association for Computing Machinery
Publication Year 2015
Year available 2015
Sub-type Published abstract
DOI 10.1145/2806416.2806418
Open Access Status Not Open Access
ISBN 9781450337946
Volume 19-23-Oct-2015
Start page 671
End page 672
Total pages 2
Chapter number 66
Total chapters 244
Formatted Abstract/Summary
Spatial trajectory data is widely available today. Over a sustained period of time, trajectory data has been collected from numerous GPS devices, smartphones, sensors and social media applications. Daily increases of real-time trajectory data have also been phenomenal in recent years. More and more new applications have emerged to derive business values from both trajectory data warehouses and real-time trajectory data. Due to their very large volumes, their nature of streaming, their highly variable levels of data quality, as well as many possible links with other types of data, making sense of spatial trajectory data becomes one of the crucial areas for big data analytics. In this paper we will present a review of the extensive work in spatiotemporal data management and trajectory mining, and discuss new challenges and new opportunities in the context
of new applications, focusing on recent advances in trajectory data management and trajectory mining from their foundations to high performance processing with modern computing infrastructure.
Keyword Trajectory data management
Trajectory mining
Q-Index Code EX
Q-Index Status Provisional Code
Institutional Status Unknown

 
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