poster

TStream: a framework for real-time and scalable trajectory stream processing and analysis

Authors:

Salman Ahmed Shaikh,

Hiroyuki Kitagawa,

Akiyoshi Matono,

Kyoung-sook KimAuthors Info & Claims

SIGSPATIAL '22: Proceedings of the 30th International Conference on Advances in Geographic Information Systems

Article No.: 30, Pages 1 - 4

https://doi.org/10.1145/3557915.3560964

Published: 22 November 2022 Publication History

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Abstract

Recent advances in location-aware devices have resulted in an exponential increase in the trajectory data streams. A number of applications require real-time processing and analysis of massive moving objects' trajectories. For instance, route guidance in emergency evacuation, patients tracking, etc. Existing scalable trajectory management systems lack support for real-time processing, while the real-time systems do not natively support spatial trajectory processing. This work presents TStream, a real-time and scalable trajectory stream processing and analysis framework. TStream utilizes grid index to support efficient processing of continuous range, kNN and join queries.

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Cited By

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Ahmadian SGollapudi SKollias KKumar VVelingker AVelusamy SChua TNgo CKumar RLauw HKa-Wei Lee R(2024)Efficient Location Sampling Algorithms for Road NetworksCompanion Proceedings of the ACM Web Conference 202410.1145/3589335.3651497(899-902)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589335.3651497
Shaikh SKitagawa HMatono AKim K(2024)A Distributed and Scalable Framework for Low-Latency Continuous Trajectory Stream ProcessingIEEE Access10.1109/ACCESS.2024.348443312(159426-159444)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3484433

Index Terms

TStream: a framework for real-time and scalable trajectory stream processing and analysis
1. Computer systems organization
  1. Real-time systems
    1. Real-time system architecture
2. Information systems
  1. Information systems applications
    1. Computing platforms
    2. Spatial-temporal systems
      1. Data streaming
      2. Geographic information systems

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Information & Contributors

Information

Published In

SIGSPATIAL '22: Proceedings of the 30th International Conference on Advances in Geographic Information Systems

November 2022

806 pages

ISBN:9781450395298

DOI:10.1145/3557915

General Chairs:
Matthias Renz
Kiel University, Germany
,
Mohamed Sarwat
Wherobots Inc. / Arizona State University

Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 22 November 2022

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Author Tags

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Poster

Funding Sources

JSPS Kakenhi
New Energy and Industrial Technology Development Organization (NEDO)
AMED
JSPS Kakenhi

Conference

SIGSPATIAL '22

Sponsor:

SIGSPATIAL

SIGSPATIAL '22: The 30th International Conference on Advances in Geographic Information Systems

November 1 - 4, 2022

Washington, Seattle

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Overall Acceptance Rate 257 of 1,238 submissions, 21%

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Cited By

View all

Ahmadian SGollapudi SKollias KKumar VVelingker AVelusamy SChua TNgo CKumar RLauw HKa-Wei Lee R(2024)Efficient Location Sampling Algorithms for Road NetworksCompanion Proceedings of the ACM Web Conference 202410.1145/3589335.3651497(899-902)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589335.3651497
Shaikh SKitagawa HMatono AKim K(2024)A Distributed and Scalable Framework for Low-Latency Continuous Trajectory Stream ProcessingIEEE Access10.1109/ACCESS.2024.348443312(159426-159444)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3484433

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Real-time, load-adaptive processing of continuous queries over data streams

Grazing trajectory statistics and visualization platform based on cloud GIS

The hyperdyadic index and generalized indexing and query with PIQUE