research-article

Scalable hybrid similarity join over geolocated time series

Authors:

Georgios Chatzigeorgakidis,

Kostas Patroumpas,

Dimitrios Skoutas,

Spiros Athanasiou,

Spiros SkiadopoulosAuthors Info & Claims

SIGSPATIAL '18: Proceedings of the 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

Pages 119 - 128

https://doi.org/10.1145/3274895.3274949

Published: 06 November 2018 Publication History

Abstract

A geolocated time series is a sequence of values associated with a geolocation, such as measurements provided by a sensor installed at a certain location. In this paper, we address the problem of hybrid similarity joins over such geolocated time series. This operation returns all pairs of geolocated time series that exhibit similar behavior in the time series domain while also being closely located in space. First, we propose algorithms for performing such join operations using different types of indices, including spatial-only, time series-only, and hybrid indices. Such centralized indexing schemes can cope well with moderate data volumes but they face scalability issues when the dataset size increases significantly. To overcome this problem, we present a MapReduce-based processing scheme with space-driven partitioning. Our parallel and distributed algorithm leverages our hybrid index for geolocated time series to efficiently execute similarity joins locally within each partition and minimize the amount of data that needs to be shuffled between processing nodes. An extensive experimental evaluation confirms that our approach can efficiently compute all matching pairs even for datasets containing millions of geolocated time series.

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Cited By

Giao B(2023)Similarity join over multiple time series under Dynamic Time WarpingCTU Journal of Innovation and Sustainable Development10.22144/ctujoisd.2023.02915:ISDS(1-11)Online publication date: 16-Oct-2023
https://doi.org/10.22144/ctujoisd.2023.029
Bachechi CPo LDesimoni F(2022)Real-Time Visual Analytics for Air QualityIntegrating Artificial Intelligence and Visualization for Visual Knowledge Discovery10.1007/978-3-030-93119-3_19(485-515)Online publication date: 5-Jun-2022
https://doi.org/10.1007/978-3-030-93119-3_19
Ma YZhang RCui ZLin C(2020)Projection Based Large Scale High-Dimensional Data Similarity Join Using MapReduce FrameworkIEEE Access10.1109/ACCESS.2020.30070288(121665-121677)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.3007028
Show More Cited By

Index Terms

Scalable hybrid similarity join over geolocated time series
1. Information systems
  1. Data management systems
    1. Data structures
  2. Information systems applications
    1. Spatial-temporal systems
2. Theory of computation
  1. Design and analysis of algorithms
    1. Distributed algorithms
    2. Parallel algorithms
      1. MapReduce algorithms

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Information & Contributors

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Published In

cover image ACM Conferences

SIGSPATIAL '18: Proceedings of the 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

November 2018

655 pages

ISBN:9781450358897

DOI:10.1145/3274895

General Chairs:
Farnoush Banaei-Kashani
University of Colorado, Denver
,
Erik Hoel
Esri
,
Program Chairs:
Ralf Hartmut Güting
FernUniversität in Hagen, Germany
,
Roberto Tamassia
Brown University
,
Li Xiong
Emory University

Copyright © 2018 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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SIGSPATIAL: ACM Special Interest Group on Spatial Information

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 06 November 2018

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Research-article

Funding Sources

General Secretariat for Research and Technology (GSRT)
Hellenic Foundation for Research and Innovation (HFRI)
EU H2020 project SLIPO (731581)
NSRF 2014-2020 project HELIX (5002781)

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SIGSPATIAL '18

Sponsor:

SIGSPATIAL

SIGSPATIAL '18: 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

November 6 - 9, 2018

Washington, Seattle

Acceptance Rates

SIGSPATIAL '18 Paper Acceptance Rate 30 of 150 submissions, 20%;

Overall Acceptance Rate 220 of 1,116 submissions, 20%

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Cited By

Giao B(2023)Similarity join over multiple time series under Dynamic Time WarpingCTU Journal of Innovation and Sustainable Development10.22144/ctujoisd.2023.02915:ISDS(1-11)Online publication date: 16-Oct-2023
https://doi.org/10.22144/ctujoisd.2023.029
Bachechi CPo LDesimoni F(2022)Real-Time Visual Analytics for Air QualityIntegrating Artificial Intelligence and Visualization for Visual Knowledge Discovery10.1007/978-3-030-93119-3_19(485-515)Online publication date: 5-Jun-2022
https://doi.org/10.1007/978-3-030-93119-3_19
Ma YZhang RCui ZLin C(2020)Projection Based Large Scale High-Dimensional Data Similarity Join Using MapReduce FrameworkIEEE Access10.1109/ACCESS.2020.30070288(121665-121677)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.3007028
Chatzigeorgakidis GSkoutas DPatroumpas KPalpanas TAthanasiou SSkiadopoulos S(2019)Local Similarity Search on Geolocated Time Series Using Hybrid IndexingProceedings of the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems10.1145/3347146.3359349(179-188)Online publication date: 5-Nov-2019
https://dl.acm.org/doi/10.1145/3347146.3359349

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