Editorial: Special Issue on Data Compression Algorithms and Their Applications
Department of Applied Mathematics and Computer Science, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark
Algorithms 2020, 13(1), 28; https://doi.org/10.3390/a13010028
Submission received: 16 January 2020
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Accepted: 16 January 2020
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Published: 17 January 2020
(This article belongs to the Special Issue Data Compression Algorithms and their Applications)
Abstract
:This Special Issue of Algorithms is focused on data compression algorithms and their applications.
1. Introduction
Data compression is classic research area in computer science focusing on the efficient storage and communication of data. Data compression is ubiquitous throughout science and engineering and essentially any data of non-trivial size is stored or communicated in compressed form on any modern computer system. With rapid advances in data collection in areas such as e-commerce, astronomy, climatology, bioinformatics, and particle physics, the need for efficient data compression is stronger than ever.
2. Special Issue
To encourage further original research on data compression algorithms and their applications, we set up a Special Issue of the MDPI journal Algorithms devoted to this topic. The call-for-papers invited articles dealing with the foundations of data compression and their applications covering the range from theory to practice. Of particular interest were articles on loss-less data compression, lossy data compression, algorithms on compressed data, compressed data structures, and applications of data compression.
All of the articles submitted to the Special Issue were evaluated by invited experts. In many cases, their detailed comments improved the technical strength and the quality of presentation. After several rounds of revisions and reviewing, seven of the submitted articles were accepted for inclusion in the Special Issue. These seven articles present new results for a wide variety of data compression topics: prefix free codes [1], finding patterns using lossy compression [2], compression in PDE solvers [3], compression and embedding of trees [4], compaction of Church numerals [5], compressed sensing [6], time-universal data compression [7]. Combined these results represent some of the current trends in the field. We hope that you will enjoy reading them and find them useful in one way or another.
Acknowledgments
Philip Bille, the Guest Editor of this Special Issue, would like to thank all researchers who submitted their work, the invited expert reviewers, MDPI, and the editorial office for their assistance.
Conflicts of Interest
The author declares no conflict of interest.
References
- Barbay, J. Optimal Prefix Free Codes with Partial Sorting. Algorithms 2019, 13, 12. [Google Scholar] [CrossRef] [Green Version]
- Rozenberg, L.; Lotan, S.; Feldman, D. Finding Patterns in Signals Using Lossy Text Compression. Algorithms 2019, 12, 267. [Google Scholar] [CrossRef] [Green Version]
- Götschel, S.; Weiser, M. Compression Challenges in Large Scale Partial Differential Equation Solvers. Algorithms 2019, 12, 197. [Google Scholar] [CrossRef] [Green Version]
- Azaïs, R. Nearest Embedded and Embedding Self-Nested Trees. Algorithms 2019, 12, 180. [Google Scholar] [CrossRef] [Green Version]
- Furuya, I.; Kida, T. Compaction of Church Numerals. Algorithms 2019, 12, 159. [Google Scholar] [CrossRef] [Green Version]
- Wang, B.; Wang, L.; Yu, H.; Xin, F. A New Regularized Reconstruction Algorithm Based on Compressed Sensing for the Sparse Underdetermined Problem and Applications of One-Dimensional and Two-Dimensional Signal Recovery. Algorithms 2019, 12, 126. [Google Scholar] [CrossRef] [Green Version]
- Ryabko, B. Time-Universal Data Compression. Algorithms 2019, 12, 116. [Google Scholar] [CrossRef] [Green Version]
© 2020 by the author. 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 (http://creativecommons.org/licenses/by/4.0/).
Share and Cite
MDPI and ACS Style
Bille, P. Editorial: Special Issue on Data Compression Algorithms and Their Applications. Algorithms 2020, 13, 28. https://doi.org/10.3390/a13010028
AMA Style
Bille P. Editorial: Special Issue on Data Compression Algorithms and Their Applications. Algorithms. 2020; 13(1):28. https://doi.org/10.3390/a13010028
Chicago/Turabian StyleBille, Philip. 2020. "Editorial: Special Issue on Data Compression Algorithms and Their Applications" Algorithms 13, no. 1: 28. https://doi.org/10.3390/a13010028
APA StyleBille, P. (2020). Editorial: Special Issue on Data Compression Algorithms and Their Applications. Algorithms, 13(1), 28. https://doi.org/10.3390/a13010028
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