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Guest Editorial Special Issue: “From Deletion-Correction to Graph Reconstruction: In Memory of Vladimir I. Levenshtein”.

Authors :
Barg, Alexander
Dolecek, Lara
Gabrys, Ryan
Katona, Gyula O. H.
Korner, Janos
McGregor, Andrew
Milenkovic, Olgica
Mesnager, Sihem
Zemor, Gilles
Source :
IEEE Transactions on Information Theory. Jun2021, Vol. 67 Issue 6, p3187-3189. 3p.
Publication Year :
2021

Abstract

There are few mathematicians whose contributions go beyond named conjectures and theorems: Vladimir Iosifovich Levenshtein ( , 1935–2017) is one such true exception. During the five decades of his active research career, he enriched combinatorics, coding, and information theory with elegant problem formulations, ingenious algorithmic solutions, and highly original proof techniques. However, his work accomplished much more—it paved the way for the creation and advancement of new scientific disciplines, such as natural language processing, metagenomics, sequence alignment, and reference-based genome assembly, as well as DNA-based data storage, to name a few. A crucial concept behind sequence alignment algorithms used in phylogeny, comparative, and cancer genomics, as well as in natural language processing is the Levenshtein (edit) distance and its extension, termed the Damerau–Levenshtein distance between strings. The Levenshtein distance equals the smallest number of insertions, deletions, or substitutions required to convert one string into another. Levenshtein introduced this metric in 1965 [item 1) in the Appendix], followed by the notion of deletion and insertion error-correcting codes that have since been used in a myriad of systems presented with synchronization errors [items 1) and 2) in the Appendix]. Levenshtein’s work also inspired the introduction of the trace reconstruction problem [items 3) and 4) in the Appendix] which has since sparked substantial interest in the field of DNA-based data storage. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
00189448
Volume :
67
Issue :
6
Database :
Academic Search Index
Journal :
IEEE Transactions on Information Theory
Publication Type :
Academic Journal
Accession number :
150448745
Full Text :
https://doi.org/10.1109/TIT.2021.3072555