research-article

Sampling strategies to create moving regions from real world observations

Authors:

Rogério Luís C. Costa,

Enrico Miranda,

José MoreiraAuthors Info & Claims

SAC '20: Proceedings of the 35th Annual ACM Symposium on Applied Computing

Pages 609 - 616

https://doi.org/10.1145/3341105.3374019

Published: 30 March 2020 Publication History

Abstract

Spatio-temporal data may be used to represent the evolution of real world objects and phenomena. Such data can be represented in discrete time, which associates spatial information (like position and shape) to time instants, or in continuous time, in which the representation of the evolution of the phenomena is decomposed into slices and interpolation functions are used to estimate the intermediate position and shape at any time. The use of a discrete model may seem more straightforward but a continuous representation provides potential gains in terms of data management, including in compression and spatio-temporal operations.

In this work, we study the use of the continuous model to represent deformable moving regions captured at discrete snapshots. We propose strategies to select the observations that should be used to define the time slices of the continuous representation, thus transforming data acquired at discrete steps into a continuous model. We also study how the use of geometry simplification mechanisms may impact on moving regions interpolation quality.

We evaluate our proposals using a dataset composed by thousands of aerial bush-fires images. After applying object simplification and slice decomposition, we use interpolation algorithms to generate in-between observations and compare them with real images. The results prove the effectiveness of our proposals and their importance in terms of interpolation accuracy.

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Cited By

da Silva HRibeiro Tde C. Costa RMoreira JSerra ESpezzano F(2024)Moving Region Representations on the Spread of a Forest FireProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679111(5343-5347)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3679111
Ribeiro TSilva Fde C. Costa R(2023)Reconstructing Spatiotemporal Data with C-VAEsAdvances in Databases and Information Systems10.1007/978-3-031-42914-9_5(59-73)Online publication date: 28-Aug-2023
https://doi.org/10.1007/978-3-031-42914-9_5
Costa RMiranda EDias PMoreira J(2021)ExperienceJournal of Data and Information Quality10.1145/342815513:1(1-13)Online publication date: 13-Jan-2021
https://dl.acm.org/doi/10.1145/3428155
Show More Cited By

Index Terms

Sampling strategies to create moving regions from real world observations
1. Information systems
  1. Data management systems
    1. Database design and models
      1. Data model extensions
        Temporal data
  2. Information systems applications
    1. Spatial-temporal systems

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cover image ACM Conferences

SAC '20: Proceedings of the 35th Annual ACM Symposium on Applied Computing

March 2020

2348 pages

ISBN:9781450368667

DOI:10.1145/3341105

Conference Chairs:
Chih-Cheng Hung
Kennesaw State University
,
Tomas Cerny
Baylor University
,
Program Chairs:
Dongwan Shin
New Mexico Tech
,
Alessio Bechini
University of Pisa, Italy

Copyright © 2020 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 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]

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Published: 30 March 2020

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SAC '20: The 35th ACM/SIGAPP Symposium on Applied Computing

March 30 - April 3, 2020

Brno, Czech Republic

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Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

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Cited By

da Silva HRibeiro Tde C. Costa RMoreira JSerra ESpezzano F(2024)Moving Region Representations on the Spread of a Forest FireProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679111(5343-5347)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3679111
Ribeiro TSilva Fde C. Costa R(2023)Reconstructing Spatiotemporal Data with C-VAEsAdvances in Databases and Information Systems10.1007/978-3-031-42914-9_5(59-73)Online publication date: 28-Aug-2023
https://doi.org/10.1007/978-3-031-42914-9_5
Costa RMiranda EDias PMoreira J(2021)ExperienceJournal of Data and Information Quality10.1145/342815513:1(1-13)Online publication date: 13-Jan-2021
https://dl.acm.org/doi/10.1145/3428155
Costa RMiranda EMoreira J(2020)Towards the automatic selection of moving regions representation methodsProceedings of the 3rd ACM SIGSPATIAL International Workshop on GeoSpatial Simulation10.1145/3423335.3428170(60-63)Online publication date: 3-Nov-2020
https://dl.acm.org/doi/10.1145/3423335.3428170
Costa RMiranda EDias PMoreira J(2020)Evaluating preprocessing and interpolation strategies to create moving regions from real-world observationsACM SIGAPP Applied Computing Review10.1145/3412816.341282020:2(46-58)Online publication date: 27-Jul-2020
https://dl.acm.org/doi/10.1145/3412816.3412820

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