- 2006—2012 СПбГУ, магистр информационных технологий. Тема диссертации: «Автоматизированная трансформация динамических SQL-запросов при реинжиниринге информационных систем».
- 2012 — 2016 СПбГУ, аспирант. Диссертация: Синтаксический анализ динамически формируемых программ
- 2013 — 2017 Старший преподаватель в СПбГУ.
- 2012 — по н.в. Исследователь в JetBrains.
- 2017 — по н.в. Доцент в СПбГУ.
Recursive Expressions for SPARQL Property Paths
Ciro Medeiros, Umberto Costa, Semyon Grigorev, Martin A. Musicante
Context-Free Path Querying by Kronecker Product
Egor Orachev, Ilya Epelbaum, Rustam Azimov, Semyon Grigorev
Context-free path queries (CFPQ) extend the regular path queries (RPQ) by allowing context-free grammars to be used as constraints for paths. Algorithms for CFPQ are actively developed, but J. Kuijpers et al. have recently concluded, that existing algorithms are not performant enough to be used in real-world applications. Thus the development of new algorithms for CFPQ is justified. In this paper, we provide a new CFPQ algorithm which is based on such linear algebra operations as Kronecker product and transitive closure and handles grammars presented as recursive state machines. Thus, the proposed algorithm can be implemented by using high-performance libraries and modern parallel hardware. Moreover, it avoids grammar growth which provides the possibility for queries optimization.
Modification of Valiant’s algorithm for the string-matching problem
Susanina Y.A., Yaveyn A.N., Grigorev S.V.
This paper aims to present Valiant’s algorithm modification, which main advantage is the possibility to divide the parsing table into successively computed layers of disjoint submatrices where each submatrix of the layer can be processed independently. Moreover, our approach is easily adapted for the string-matching problem.
Context-Free Path Querying with Single-Path Semantics by Matrix Multiplication
Arseniy Terekhov, Artyom Khoroshev, Rustam Azimov, Semyon Grigorev
A recent study showed that the applicability of context-free path querying (CFPQ) algorithms with relational query semantics integrated with graph databases is limited because of low performance and high memory consumption of existing solutions. In this work, we implement a matrix-based CFPQ algorithm by using appropriate high-performance libraries for linear algebra and integrate it with RedisGraph graph database. Also, we introduce a new CFPQ algorithm with single-path query semantics that allows us to extract one found path for each pair of nodes. Finally, we provide the evaluation of our algorithms for both semantics which shows that matrix-based CFPQ implementation for Redis-Graph database is performant enough for real-world data analysis.
Optimizing GPU programs by partial evaluation
Aleksey Tyurin, Daniil Berezun, Semyon Grigorev
While GPU utilization allows one to speed up computations to the orders of magnitude, memory management remains the bottleneck making it often a challenge to achieve the desired performance. Hence, different memory optimizations are leveraged to make memory being used more effectively. We propose an approach automating memory management utilizing partial evaluation, a program transformation technique that enables data accesses to be pre-computed, optimized, and embedded into the code, saving memory transactions. An empirical evaluation of our approach shows that the transformed program could be up to 8 times as efficient as the original one in the case of CUDA C naïve string pattern matching algorithm implementation.
Path Querying with Conjunctive Grammars by Matrix Multiplication
R. Azimov and S. Grigorev
Path querying with conjunctive grammars is known to be undecidable. There is an algorithm for path querying with linear conjunctive grammars which provides an over-approximation of the result, but there is no algorithm for arbitrary conjunctive grammars. We propose the first algorithm for path querying with arbitrary conjunctive grammars. The proposed algorithm is matrix-based and allows us to efficiently apply GPGPU computing techniques and other optimizations for matrix operations.
Improved Architecture of Artificial Neural Network for Secondary Structure Analysis
Semyon Grigorev and Polina Lunina
Bar-Hillel Theorem Mechanization in Coq
Sergey Bozhko, Leyla Khatbullina, Semyon Grigorev
The Bar-Hillel theorem states that context-free languages are closed under intersection with a regular set. This theorem has a constructive proof and thus provides a formal justification of correctness of the algorithms for applications mentioned above. Mechanization of the Bar-Hillel theorem, therefore, is both a fundamental result of formal language theory and a basis for the certified implementation of the algorithms for applications. In this work, we present the mechanized proof of the Bar-Hillel theorem in Coq.
Evaluation of the Context-Free Path Querying Algorithm Based on Matrix Multiplication
Nikita Mishin, Iaroslav Sokolov, Egor Spirin, Vladimir Kutuev, Egor Nemchinov, Sergey Gorbatyuk, and Semyon Grigorev
The Composition of Dense Neural Networks and Formal Grammars for Secondary Structure Analysis
Semyon Grigorev and Polina Lunina
We propose a way to combine formal grammars and artificial neural networks for biological sequences processing. Formal grammars encode the secondary structure of the sequence and neural networks deal with mutations and noise. In contrast to the classical way, when probabilistic grammars are used for secondary structure modeling, we propose to use arbitrary (not probabilistic) grammars which simplifies grammar creation. Instead of modeling the structure of the whole sequence, we create a grammar which only describes features of the secondary structure. Then we use matrix-based parsing to extract features: the fact that some substring can be derived from some nonterminal is a feature. After that, we use a dense neural network to process features.
Path querying on acyclic graphs using Boolean grammars
Shemetova E.N., Grigorev S.V.
One of the problems in graph data analysis is querying for specific paths. Such queries are usually performed by means of a formal grammar that describes the allowed edge-labeling of the paths. Path query is said to be calculated using relational query semantics if it is evaluated to triple ((A,v1,v2), such that there is a path from v1 to v2 such that the labels on the edges of this path form a string derivable from the nonterminal A. We focus on the Boolean languages that use Boolean grammars to describe the labeling of paths. Although path querying using relational query semantics and Boolean grammars is known to be undecidable, in this work we propose a path querying algorithm on acyclic graphs which uses relational query semantics and Boolean grammars and approximates the exact solution. To achieve better performance in compare with the naive algorithm, considered classes of graphs were limited to acyclic graphs.
Parser combinators for context-free path querying
Ekaterina Verbitskaia, Ilya Kirillov, Ilya Nozkin, Semyon Grigorev
Transparent integration of a domain-specific language for specification of context-free path queries (CFPQs) into a general-purpose programming language as well as static checking of errors in queries may greatly simplify the development of applications using CFPQs. LINQ and ORM can be used for the integration, but they have issues with flexibility: query decomposition and reusing of subqueries are a challenge. Adaptation of parser combinators technique for paths querying may solve these problems. Conventional parser combinators process linear input, and only the Trails library is known to apply this technique for path querying. We demonstrate that it is possible to create general parser combinators for CFPQ which support arbitrary context-free grammars and arbitrary input graphs. We implement a library of such parser combinators and show that it is applicable for realistic tasks.
Extended Abstract: F# OpenCL Type Provider
Kirill Smirenko, Semyon Grigorev
Extended abstract at TyDe 2018 (at ICFP).
Context-free path querying by matrix multiplication
Rustam Azimov, Semyon Grigorev
Context-Free Path Querying with Structural Representation of Result
Semyon Grigorev, Anastasiya Ragozina
There are several solutions for CFPQ, but how to provide structural representation of query result which is practical for answer processing and debugging is still an open problem. In this paper we propose a graph parsing technique which allows one to build such representation with respect to given grammar in polynomial time and space for arbitrary context-free grammar and graph. Proposed algorithm is based on generalized LL parsing algorithm, while previous solutions are based mostly on CYK or Earley algorithms, which reduces time complexity in some cases.
Graph Parsing by Matrix Multiplication
Rustam Azimov, Semyon Grigorev
Certified Grammar Transformation to Chomsky Normal Form in F*
Marina Polubelova, Sergey Bozhko, Semyon Grigorev
Relaxed Parsing of Regular Approximations of String-Embedded Languages
Ekaterina Verbitskaia , Semyon Grigorev, Dmitry Avdyukhin
We present a technique for syntax analysis of a regular set of input strings. This problem is relevant for the analysis of string-embedded languages when a host program generates clauses of embedded language at run time. Our technique is based on a generalization of RNGLR algorithm, which, inherently, allows us to construct a finite representation of parse forest for regularly approximated set of input strings. This representation can be further utilized for semantic analysis and transformations in the context of reengineering, code maintenance, program understanding etc. The approach in question implements relaxed parsing: non-recognized strings in approximation set are ignored with no error detection.
Lexical Analysis of Dynamically Generated String Expressions
Marina Polubelova, Semyon Grigorev
Generalized Table-based LL-parsing
Ragozina Anastasiya, Grigorev Semyon
On development of static analysis tools for string-embedded languages
Marat Khabibullin, Andrei Ivanov, Semyon Grigorev
String-embedded Language Support in Integrated Development Environment
Semen Grigorev, Ekaterina Verbitskaia, Andrei Ivanov, Marina Polubelova, and Ekaterina Mavchun
GLR-based Abstract Parsing
Semen Grigorev and Iakov Kirilenko