short-paper

CoMing: A Real-time Co-Movement Mining System for Streaming Trajectories

Authors:

Ziquan Fang,

Yunjun Gao,

Lu Pan,

Lu Chen,

Xiaoye Miao,

Christian S. JensenAuthors Info & Claims

SIGMOD '20: Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data

Pages 2777 - 2780

https://doi.org/10.1145/3318464.3384703

Published: 31 May 2020 Publication History

Get Access

Abstract

The aim of real-time co-movement pattern mining for streaming trajectories is to discover co-moving objects that satisfy specific spatio-temporal constraints in real time. This functionality serves a range of real-world applications, such as traffic monitoring and management. However, little work targets the visualization and interaction with such co-movement detection on streaming trajectories. To this end, we develop CoMing, a real-time co-movement pattern mining system, to handle streaming trajectories. CoMing leverages ICPE, a real-time distributed co-movement pattern detection framework, and thus, it has its capacity of good performance. This demonstration offers hands-on experience with CoMing's visual and user-friendly interface. Moreover, several applications in the traffic domain, including object monitoring and traffic statistics visualization, are also provided to users.

References

[1]

Lu Chen, Yunjun Gao, Ziquan Fang, Xiaoye Miao, Christian S. Jensen, and Chenjuan Guo. 2019. Real-time Distributed Co-Movement Pattern Detection on Streaming Trajectories. PVLDB 12, 10 (2019), 1208--1220.

Digital Library

Google Scholar

[2]

Lei Duan, Tinghai Pang, Jyrki Nummenmaa, Jie Zuo, Peng Zhang, and Changjie Tang. 2018. Bus-OLAP: A Data Management Model for Nonon- Time Events Query Over Bus Journey Data. Data Science and Engineering 3, 1 (2018), 52--67.

Crossref

Google Scholar

[3]

Hoyoung Jeung, Man Lung Yiu, Xiaofang Zhou, Christian S. Jensen, and Heng Tao Shen. 2008. Discovery of Convoys in Trajectory Databases. PVLDB 1, 1 (2008), 1068--1080.

Digital Library

Google Scholar

[4]

Zeyuan Shang, Guoliang Li, and Zhifeng Bao. 2018. DITA: Distributed In-Memory Trajectory Analytics. In Proceedings of the 2018 International Conference on Management of Data (SIGMOD). 725--740.

Digital Library

Google Scholar

Cited By

View all

Wu YMeng YLi YGuo LZhu XFournier-Viger PWu X(2024) COPP-Miner: Top- K Contrast Order-Preserving Pattern Mining for Time Series Classification IEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.3321749(1-16)Online publication date: 2024
https://doi.org/10.1109/TKDE.2023.3321749
Gao YFang ZXu JGong SShen CChen L(2024)An Efficient and Distributed Framework for Real-Time Trajectory Stream ClusteringIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.3312319(1-17)Online publication date: 2024
https://doi.org/10.1109/TKDE.2023.3312319
Yao DWang JChen WGuo FHan PBi J(2024)Deep Dirichlet Process Mixture Model for Non-parametric Trajectory Clustering2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00339(4449-4462)Online publication date: 13-May-2024
https://doi.org/10.1109/ICDE60146.2024.00339
Show More Cited By

Index Terms

CoMing: A Real-time Co-Movement Mining System for Streaming Trajectories
1. Information systems
  1. Information systems applications
    1. Data mining
      1. Data stream mining

Recommendations

Real-time distributed co-movement pattern detection on streaming trajectories

With the widespread deployment of mobile devices with positioning capabilities, increasingly massive volumes of trajectory data are being collected that capture the movements of people and vehicles. This data enables co-movement pattern detection, which ...
Efficient mining of regional movement patterns in semantic trajectories
Proceedings of the 43rd International Conference on Very Large Data Bases, Munich, Germany

Semantic trajectory pattern mining is becoming more and more important with the rapidly growing volumes of semantically rich trajectory data. Extracting sequential patterns in semantic trajectories plays a key role in understanding semantic behaviour of ...
Mining Partners in Trajectories

Spatiotemporal data is everywhere, being gathered from different devices such as Earth Observation and GPS satellites, sensor networks and mobile gadgets. Spatiotemporal data collected from moving objects is of particular interest for a broad range of ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

SIGMOD '20: Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data

June 2020

2925 pages

ISBN:9781450367356

DOI:10.1145/3318464

General Chairs:
David Maier
Portland State University, USA
,
Rachel Pottinger
University of British Columbia, Canada
,
Program Chairs:
AnHai Doan
University of Wisconsin, USA
,
Wang-Chiew Tan
Megagon Labs, USA
,
Publications Chairs:
Abdussalam Alawini
University of Illinois at Urbana-Champaign, USA
,
Hung Q. Ngo
RelationalAI, USA

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 31 May 2020

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Short-paper

Conference

SIGMOD/PODS '20

Sponsor:

SIGMOD

SIGMOD/PODS '20: International Conference on Management of Data

June 14 - 19, 2020

OR, Portland, USA

Acceptance Rates

Overall Acceptance Rate 785 of 4,003 submissions, 20%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

11
Total Citations
View Citations
501
Total Downloads

Downloads (Last 12 months)58
Downloads (Last 6 weeks)5

Reflects downloads up to 22 Sep 2024

Other Metrics

View Author Metrics

Citations

Cited By

View all

Wu YMeng YLi YGuo LZhu XFournier-Viger PWu X(2024) COPP-Miner: Top- K Contrast Order-Preserving Pattern Mining for Time Series Classification IEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.3321749(1-16)Online publication date: 2024
https://doi.org/10.1109/TKDE.2023.3321749
Gao YFang ZXu JGong SShen CChen L(2024)An Efficient and Distributed Framework for Real-Time Trajectory Stream ClusteringIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.3312319(1-17)Online publication date: 2024
https://doi.org/10.1109/TKDE.2023.3312319
Yao DWang JChen WGuo FHan PBi J(2024)Deep Dirichlet Process Mixture Model for Non-parametric Trajectory Clustering2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00339(4449-4462)Online publication date: 13-May-2024
https://doi.org/10.1109/ICDE60146.2024.00339
Portugal IAlencar PCowan D(2024)A framework for spatial-temporal cluster evolution representation and analysis based on graphsScientific Reports10.1038/s41598-024-72504-x14:1Online publication date: 27-Sep-2024
https://doi.org/10.1038/s41598-024-72504-x
Han JLu WGe KZhu MChen WLiu YWu F(2023)A pattern accumulated compression method for trajectories constrained by urban road networksData & Knowledge Engineering10.1016/j.datak.2023.102143145(102143)Online publication date: May-2023
https://doi.org/10.1016/j.datak.2023.102143
Zhang DChang ZYang DLi DTan KChen KChen G(2023)SQUID: subtrajectory query in trillion-scale GPS databaseThe VLDB Journal10.1007/s00778-022-00777-732:4(887-904)Online publication date: 19-Jan-2023
https://doi.org/10.1007/s00778-022-00777-7
Trisminingsih RShaikh SAmagasa TKitagawa HMatono A(2023)TraPM: A Framework for Online Pattern Matching Over Trajectory StreamsInformation Integration and Web Intelligence10.1007/978-3-031-48316-5_45(510-525)Online publication date: 22-Nov-2023
https://doi.org/10.1007/978-3-031-48316-5_45
Feng CPan ZFang JXu JZhao PZhao LAl Hasan MXiong L(2022)AriesProceedings of the 31st ACM International Conference on Information & Knowledge Management10.1145/3511808.3557239(499-508)Online publication date: 17-Oct-2022
https://dl.acm.org/doi/10.1145/3511808.3557239
Yin HGao HWang BLi SLi J(2022)Efficient trajectory compression and range query processingWorld Wide Web10.1007/s11280-022-01038-x25:3(1259-1285)Online publication date: 30-Mar-2022
https://dl.acm.org/doi/10.1007/s11280-022-01038-x
Tritsarolis AChondrodima ETampakis PPikrakis ATheodoridis Y(2022)Predicting Co-movement patterns in mobility dataGeoInformatica10.1007/s10707-022-00478-x28:2(221-243)Online publication date: 22-Sep-2022
https://doi.org/10.1007/s10707-022-00478-x
Show More Cited By

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Cited By

Index Terms

Recommendations

Real-time distributed co-movement pattern detection on streaming trajectories

Efficient mining of regional movement patterns in semantic trajectories

Mining Partners in Trajectories