Demo Data from VLDB 2026 / reviewers in the wild / expert
Anatoly V. Anisimov
dblp:72/5294 · also A. V. Anisimov, Anatoly Anisimov
· DBLP profile ↗
13ranked-venue papers
7as first author
0since 2021 · last 2020
0000-0002-1467-2006ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Databases, data management, data science and information retrieval · 8 · 2 first-authorTheory of computation · 5 · 5 first-authorArtificial intelligence and machine learning · 4 · 1 first-authorGraphics, computer vision, multimedia, augmented reality and games · 2
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Theoretical computer science
1 paper |
Coding theory · 100% |
Topics — the 5 heaviest of 5, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Coding theory › source coding › variable-length codes
integer encoding |
0.3 | 1 | 2017 | Variable-Length Prefix Codes With Multiple Delimiters · IEEE Trans. Inf. Theory 2017 |
Coding theory › source coding › variable-length codes
prefix codes |
0.3 | 1 | 2017 | Variable-Length Prefix Codes With Multiple Delimiters · IEEE Trans. Inf. Theory 2017 |
Coding theory
source coding |
0.3 | 1 | 2017 | Variable-Length Prefix Codes With Multiple Delimiters · IEEE Trans. Inf. Theory 2017 |
Coding theory › source coding
variable-length codes |
0.3 | 1 | 2017 | Variable-Length Prefix Codes With Multiple Delimiters · IEEE Trans. Inf. Theory 2017 |
Coding theory › error-correcting codes › decoding
decoding algorithms |
0.1 | 1 | 2017 | Variable-Length Prefix Codes With Multiple Delimiters · IEEE Trans. Inf. Theory 2017 |
Methods — techniques the papers use, named apart from their topics
simulation · 0.3combinatorial analysis · 0.3
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2020 | Reverse Multi-Delimiter Compression CodesabstractAn enhanced version of a recently introduced family of variable length binary codes with multiple pattern delimiters is presented and discussed. These codes are complete, universal, synchronizable, they have monotonic indexing and allow a standard search in compressed files. Comparing the compression rate on natural language texts demonstrates that introduced codes appear to be much superior to other known codes with similar properties. A fast byte-aligned decoding algorithm is constructed, which operates much faster than the one for Fibonacci codes. Igor O. Zavadskyi, Anatoly V. Anisimov |
DCC | 2 |
| 2018 | English Text Parsing by Means of Error Correcting Automaton
Oleksandr Marchenko, Anatoly V. Anisimov, Igor O. Zavadskyi, Egor Melnikov |
NLDB | 2 |
| 2017 | Machine Learning Method for Paraphrase Identification
Oleksandr Marchenko, Anatoly V. Anisimov, Andrii Nykonenko, Tetiana Rossada, Egor Melnikov |
FQAS | 2 |
| 2017 | Authorship Attribution System
Oleksandr Marchenko, Anatoly V. Anisimov, Andrii Nykonenko, Tetiana Rossada, Egor Melnikov |
NLDB | 2 |
| 2017 | Variable-Length Prefix Codes With Multiple DelimitersabstractLet m1, m2, . . . , mt be a fixed set of natural integers given in ascending order. A multi-delimiter code Dm1,...,mtconsists of t words of the form 1mi0 and all other binary words with suffixes having one of the forms 01mi0 that cannot occur in other places of a word, i = 1, . . . , t. Completeness, universality, and density characteristics of multi-delimiter codes are proved. Encoding of integers by multi-delimiter codes is considered in detail. For these codes, fast byte aligned decoding algorithms are also discussed. The comparison of compression rate and decoding speed of Fibonacci codes, (s, c)-dense codes and different multi-delimiter codes is presented. By many useful properties, multi-delimiter codes are superior to Fibonacci codes. Anatoly V. Anisimov, Igor O. Zavadskyi |
IEEE Trans. Inf. Theory | 1 |
| 2014 | Semantic and Syntactic Model of Natural Language Based on Tensor Factorization
Anatoly V. Anisimov, Oleksandr Marchenko, Volodymyr Taranukha, Taras Vozniuk |
NLDB | 1 |
| 2013 | Ukrainian WordNet: Creation and Filling
Anatoly V. Anisimov, Oleksandr Marchenko, Andrey Nikonenko, Elena Porkhun, Volodymyr Taranukha |
FQAS | 1 |
| 2013 | Prefix Encoding by Means of the $(2, 3)$-Representation of NumbersabstractA new family of universal self-synchronizable variable-length codes is introduced. This family is not a generalization or improvement of the existing prefix codes, but is based on a new method of integer representation in a mixed base using the radix-2 and the auxiliary radix-3. Upper length bounds for such codes are obtained. The asymptotic estimates for the (2,3)-encoding including the pointwise redundancy are also given. In particular, this implies that the average length of a (2,3)-codeword is shorter than that of Fibonacci code. Elias gamma and delta codes are adopted for the (2,3) -variant with asymptotically shorter codewords as against the original case. Improvement of gamma and delta encoding for all numbers is also presented. One of (2,3)-codes with high density is highlighted as a possible candidate for practical use in data compression. The (2,3) -codes are very simple to construct and they have evident features of strong robustness. Anatoly V. Anisimov |
IEEE Trans. Inf. Theory | 1 |
| 2012 | GPU-Accelerated Non-negative Matrix Factorization for Text Mining
Volodymyr Kysenko, Karl Rupp, Oleksandr Marchenko, Siegfried Selberherr, Anatoly V. Anisimov |
NLDB | 5 |
| 2003 | On the Average Redundancy Rate of Adaptive Block Codes under Mixed SourcesabstractSummary form only given. The average redundancy rate of Krichevsky's sample-based universal block codes was analyzed, in a situation wherein the samples and block codes for compression were produced from two different memoryless sources. It was proven that the average redundancy rate of adaptive block codes O/sub /spl lscr/,T/, constructed using samples of length /spl lscr/ from a source T, were used to encode block codes of length n from a source S. The adaptive block codes, constructed using samples from a source T and applied to a source S, have achieved lower average redundancy than universal codes. Yuriy A. Reznik, Anatoly V. Anisimov |
DCC | 2 |
| 1987 | Stable Finite Automata Mappings and Church-Rosser Systems
Anatoly V. Anisimov, Vladimir A. Oleshchuk |
FCT | 1 |
| 1975 | Languages Over Free Groups
Anatoly V. Anisimov |
MFCS | 1 |
| 1973 | Top-down Analysis of Programming Languages without Backtrack (in Russian)
Anatoly V. Anisimov |
MFCS | 1 |