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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...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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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. |
format | Online Article Text |
id | pubmed-9140898 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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