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A Hybrid Data-Differencing and Compression Algorithm for the Automotive Industry

We propose an innovative delta-differencing algorithm that combines software-updating methods with LZ77 data compression. This software-updating method relates to server-side software that creates binary delta files and to client-side software that performs software-update installations. The propose...

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Detalles Bibliográficos
Autores principales: Belu, Sabin, Coltuc, Daniela
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9140898/
https://www.ncbi.nlm.nih.gov/pubmed/35626459
http://dx.doi.org/10.3390/e24050574
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author Belu, Sabin
Coltuc, Daniela
author_facet Belu, Sabin
Coltuc, Daniela
author_sort Belu, Sabin
collection PubMed
description We propose an innovative delta-differencing algorithm that combines software-updating methods with LZ77 data compression. This software-updating method relates to server-side software that creates binary delta files and to client-side software that performs software-update installations. The proposed algorithm creates binary-differencing streams already compressed from an initial phase. We present a software-updating method suitable for OTA software updates and the method’s basic strategies to achieve a better performance in terms of speed, compression ratio or a combination of both. A comparison with publicly available solutions is provided. Our test results show our method, Keops, can outperform an LZMA (Lempel–Ziv–Markov chain-algorithm) based binary differencing solution in terms of compression ratio in two cases by more than 3% while being two to five times faster in decompression. We also prove experimentally that the difference between Keops and other competing delta-creator software increases when larger history buffers are used. In one case, we achieve a three times better performance for a delta rate compared to other competing delta rates.
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spelling pubmed-91408982022-05-28 A Hybrid Data-Differencing and Compression Algorithm for the Automotive Industry Belu, Sabin Coltuc, Daniela Entropy (Basel) Article We propose an innovative delta-differencing algorithm that combines software-updating methods with LZ77 data compression. This software-updating method relates to server-side software that creates binary delta files and to client-side software that performs software-update installations. The proposed algorithm creates binary-differencing streams already compressed from an initial phase. We present a software-updating method suitable for OTA software updates and the method’s basic strategies to achieve a better performance in terms of speed, compression ratio or a combination of both. A comparison with publicly available solutions is provided. Our test results show our method, Keops, can outperform an LZMA (Lempel–Ziv–Markov chain-algorithm) based binary differencing solution in terms of compression ratio in two cases by more than 3% while being two to five times faster in decompression. We also prove experimentally that the difference between Keops and other competing delta-creator software increases when larger history buffers are used. In one case, we achieve a three times better performance for a delta rate compared to other competing delta rates. MDPI 2022-04-19 /pmc/articles/PMC9140898/ /pubmed/35626459 http://dx.doi.org/10.3390/e24050574 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Belu, Sabin
Coltuc, Daniela
A Hybrid Data-Differencing and Compression Algorithm for the Automotive Industry
title A Hybrid Data-Differencing and Compression Algorithm for the Automotive Industry
title_full A Hybrid Data-Differencing and Compression Algorithm for the Automotive Industry
title_fullStr A Hybrid Data-Differencing and Compression Algorithm for the Automotive Industry
title_full_unstemmed A Hybrid Data-Differencing and Compression Algorithm for the Automotive Industry
title_short A Hybrid Data-Differencing and Compression Algorithm for the Automotive Industry
title_sort hybrid data-differencing and compression algorithm for the automotive industry
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9140898/
https://www.ncbi.nlm.nih.gov/pubmed/35626459
http://dx.doi.org/10.3390/e24050574
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