A Cloud-Based Trajectory Data Management System?

Ruiyuan Li; Sijie Ruan; Jie Bao; Yu Zheng

doi:10.1145/3139958.3139990

A Cloud-Based Trajectory Data Management System^?

Ruiyuan Li, Sijie Ruan, Jie Bao, Yu Zheng^*

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

18 Citations (Scopus)

Abstract

With the rapid development of location-acquisition techniques, massive trajectories are continuously generated. Many urban applications rely heavily on the data mining/analysis results of massive trajectory data. This demo presents a holistic data management system for both historical and real-time trajectory records based on a cloud platform, such as Microsoft Azure. The proposed system is able to efficiently support a variety of trajectory queries, including ID-Temporal query, Spatio-Temporal query, and Path-Temporal query. With these queries, we demonstrate that different urban applications can be realized in a much easier way.

Original language	English
Title of host publication	GIS
Subtitle of host publication	Proceedings of the ACM International Symposium on Advances in Geographic Information Systems
Editors	Siva Ravada, Erik Hoel, Roberto Tamassia, Shawn Newsam, Goce Trajcevski, Goce Trajcevski
Publisher	Association for Computing Machinery
ISBN (Print)	9781450354905
DOIs	https://doi.org/10.1145/3139958.3139990
Publication status	Published - 7 Nov 2017
Externally published	Yes
Event	25th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2017 - Redondo Beach, United States Duration: 7 Nov 2017 → 10 Nov 2017

Publication series

Name	GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems
Volume	2017-November

Conference

Conference	25th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2017
Country/Territory	United States
City	Redondo Beach
Period	7/11/17 → 10/11/17

Keywords

Cloud Computing
Trajectory Data Management

Access to Document

10.1145/3139958.3139990

Cite this

Li, R., Ruan, S., Bao, J., & Zheng, Y. (2017). A Cloud-Based Trajectory Data Management System^?. In S. Ravada, E. Hoel, R. Tamassia, S. Newsam, G. Trajcevski, & G. Trajcevski (Eds.), GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems Article 96 (GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems; Vol. 2017-November). Association for Computing Machinery. https://doi.org/10.1145/3139958.3139990

Li, Ruiyuan ; Ruan, Sijie ; Bao, Jie et al. / A Cloud-Based Trajectory Data Management System^?. GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems. editor / Siva Ravada ; Erik Hoel ; Roberto Tamassia ; Shawn Newsam ; Goce Trajcevski ; Goce Trajcevski. Association for Computing Machinery, 2017. (GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems).

@inproceedings{c9db0306f075414483cb14af5549ceb3,

title = "A Cloud-Based Trajectory Data Management System?",

abstract = "With the rapid development of location-acquisition techniques, massive trajectories are continuously generated. Many urban applications rely heavily on the data mining/analysis results of massive trajectory data. This demo presents a holistic data management system for both historical and real-time trajectory records based on a cloud platform, such as Microsoft Azure. The proposed system is able to efficiently support a variety of trajectory queries, including ID-Temporal query, Spatio-Temporal query, and Path-Temporal query. With these queries, we demonstrate that different urban applications can be realized in a much easier way.",

keywords = "Cloud Computing, Trajectory Data Management",

author = "Ruiyuan Li and Sijie Ruan and Jie Bao and Yu Zheng",

note = "Publisher Copyright: {\textcopyright} 2017 Copyright held by the owner/author(s).; 25th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2017 ; Conference date: 07-11-2017 Through 10-11-2017",

year = "2017",

month = nov,

day = "7",

doi = "10.1145/3139958.3139990",

language = "English",

isbn = "9781450354905",

series = "GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems",

publisher = "Association for Computing Machinery",

editor = "Siva Ravada and Erik Hoel and Roberto Tamassia and Shawn Newsam and Goce Trajcevski and Goce Trajcevski",

booktitle = "GIS",

}

Li, R, Ruan, S, Bao, J & Zheng, Y 2017, A Cloud-Based Trajectory Data Management System^?. in S Ravada, E Hoel, R Tamassia, S Newsam, G Trajcevski & G Trajcevski (eds), GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems., 96, GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems, vol. 2017-November, Association for Computing Machinery, 25th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2017, Redondo Beach, United States, 7/11/17. https://doi.org/10.1145/3139958.3139990

A Cloud-Based Trajectory Data Management System^?. / Li, Ruiyuan; Ruan, Sijie; Bao, Jie et al.
GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems. ed. / Siva Ravada; Erik Hoel; Roberto Tamassia; Shawn Newsam; Goce Trajcevski; Goce Trajcevski. Association for Computing Machinery, 2017. 96 (GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems; Vol. 2017-November).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review