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2015 IEEE 31st International Conference on Data Engineering (ICDE) (2015)
Seoul, South Korea
April 13, 2015 to April 17, 2015
ISBN: 978-1-4799-7964-6
pp: 1448-1451
Jiansong Ma , Shanghai Key Laboratory of Trustworthy Computing, East China Normal University, China
Yu Cao , EMC Labs, Beijing, China
Xiaoling Wang , Shanghai Key Laboratory of Trustworthy Computing, East China Normal University, China
Chaoyong Wang , Shanghai Key Laboratory of Trustworthy Computing, East China Normal University, China
Cheqing Jin , Shanghai Key Laboratory of Trustworthy Computing, East China Normal University, China
Aoying Zhou , Shanghai Key Laboratory of Trustworthy Computing, East China Normal University, China
ABSTRACT
In modern cities, more and more people drive the vehicles, equipped with the GPS devices, which create a large scale of trajectories. Gathering and analyzing these large-scale trajectory data provide a new opportunity to understand the city dynamics and to reveal the hidden social and economic phenomena. This paper designs and implements a tool, named as PGWinFunc, to analyze trajectory data by extending a traditional relational database. Firstly we introduce some efficient query process and optimization methods for SQL Window Aggregate Functions in PostgreSQL. Secondly, we present how to mine the LBS (Location-Based Service) patterns, such as the average speed and traffic flow, from the large-scale trajectories with SQL expression with Window Aggregate Functions. Finally, the effectiveness and efficiency of the PGWinFunc tool are demonstrated and we also visualized the results by BAIDU MAP.
INDEX TERMS
Aggregates, Trajectory, Roads, Optimization, Cities and towns, Data visualization, Sequential analysis
CITATION

J. Ma, Y. Cao, X. Wang, C. Wang, C. Jin and A. Zhou, "PGWinFunc: Optimizing Window Aggregate Functions in PostgreSQL and its application for trajectory data," 2015 IEEE 31st International Conference on Data Engineering (ICDE), Seoul, South Korea, 2015, pp. 1448-1451.
doi:10.1109/ICDE.2015.7113398
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