TY - GEN
T1 - PGWinFunc
T2 - 2015 31st IEEE International Conference on Data Engineering, ICDE 2015
AU - Ma, Jiansong
AU - Cao, Yu
AU - Wang, Xiaoling
AU - Wang, Chaoyong
AU - Jin, Cheqing
AU - Zhou, Aoying
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/5/26
Y1 - 2015/5/26
N2 - 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.
AB - 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.
UR - https://www.scopus.com/pages/publications/84940845593
U2 - 10.1109/ICDE.2015.7113398
DO - 10.1109/ICDE.2015.7113398
M3 - 会议稿件
AN - SCOPUS:84940845593
T3 - Proceedings - International Conference on Data Engineering
SP - 1448
EP - 1451
BT - 2015 IEEE 31st International Conference on Data Engineering, ICDE 2015
PB - IEEE Computer Society
Y2 - 13 April 2015 through 17 April 2015
ER -