research-article

Trajectory-aware Load Adaption for Continuous Traffic Analytics

Authors:

Kostas Patroumpas,

Serafeim PapadiasAuthors Info & Claims

SSTD '19: Proceedings of the 16th International Symposium on Spatial and Temporal Databases

Pages 70 - 79

https://doi.org/10.1145/3340964.3340967

Published: 19 August 2019 Publication History

Abstract

We introduce a framework for online monitoring of moving objects, which takes into account their evolving trajectories and copes smoothly with fluctuating demands of multiple continuous queries for limited system resources. This centralized scheme accepts streaming positional updates from numerous objects, but it only examines recent trajectory segments with expectedly higher utility in query evaluation, shedding the rest as immaterial. We focus on adaptive processing under extreme load conditions, opting to retain salient trajectory segments and possibly sacrifice smaller, frequently observed paths in favor of longer, distinctive routes. We propose heuristics for incremental, yet approximate, query evaluation in order to provide up-to-date traffic analytics using windows that abstract particular regions and time intervals of interest. Finally, we conduct a comprehensive experimental study to validate our approach, demonstrating its benefits in result accuracy and efficiency for almost real-time response to trajectory-based aggregates.

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Cited By

Papadias SKaoudi ZQuiané-Ruiz JMarkl V(2022)Space-efficient random walks on streaming graphsProceedings of the VLDB Endowment10.14778/3565816.356583516:2(356-368)Online publication date: 23-Nov-2022
https://doi.org/10.14778/3565816.3565835
Fitters WCuzzocrea AHassani M(2021)Enhancing LSTM Prediction of Vehicle Traffic Flow Data via Outlier Correlations2021 IEEE 45th Annual Computers, Software, and Applications Conference (COMPSAC)10.1109/COMPSAC51774.2021.00039(210-217)Online publication date: Jul-2021
https://doi.org/10.1109/COMPSAC51774.2021.00039

Index Terms

Trajectory-aware Load Adaption for Continuous Traffic Analytics
1. Information systems
  1. Information systems applications
    1. Spatial-temporal systems
      1. Data streaming
      2. Location based services

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cover image ACM Other conferences

SSTD '19: Proceedings of the 16th International Symposium on Spatial and Temporal Databases

August 2019

245 pages

ISBN:9781450362801

DOI:10.1145/3340964

Copyright © 2019 ACM.

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 the author(s) 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].

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TU Wien: TU Wien

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Published: 19 August 2019

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SSTD '19

SSTD '19: 16th International Symposium on Spatial and Temporal Databases

August 19 - 21, 2019

Vienna, Austria

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Cited By

Papadias SKaoudi ZQuiané-Ruiz JMarkl V(2022)Space-efficient random walks on streaming graphsProceedings of the VLDB Endowment10.14778/3565816.356583516:2(356-368)Online publication date: 23-Nov-2022
https://doi.org/10.14778/3565816.3565835
Fitters WCuzzocrea AHassani M(2021)Enhancing LSTM Prediction of Vehicle Traffic Flow Data via Outlier Correlations2021 IEEE 45th Annual Computers, Software, and Applications Conference (COMPSAC)10.1109/COMPSAC51774.2021.00039(210-217)Online publication date: Jul-2021
https://doi.org/10.1109/COMPSAC51774.2021.00039

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