Публикации
 St. Petersburg State Polytechnical University Journal. Computer Science. Telecommunications and Control Systems,

Computational Stochastic Modeling of Cellular Microtubule Network46th Annual Meeting of the American Society for Cell Biology,

Computersupported collaborative learning with mindmapsCommunications in Computer and Information Science 17 CCIS, pp. 478489,
 In: Benczúr A. et al. (eds) New Trends in Databases and Information Systems. ADBIS 2018. Communications in Computer and Information Science, vol 909. Springer, Cham,
 Proceedings of International Conference on Algorithms for Computational Biology.  2015.  141153,

Constructing verificationoriented domainspecific process ontologiesSystem Informatics. Iss. 14. 2019. A.P. Ershov Institute of Informatics Systems, Siberian Branch of the Russian Academy of Sciences, Novosibirsk. P. 19–30.,

The 27th ACM International Conference on Information and Knowledge Management (CIKM ’18), October 22–26, 2018, Torino, Italy. ACM, New York, NY, USA, 10 pages
 Proceedings of the 2019 miniKanren and Relational Programming Workshop,

ADBIS 2020. Advances in Databases and Information Systems. Lecture Notes in Computer Science.,
Contextfree path queries (CFPQ) extend the regular path queries (RPQ) by allowing contextfree 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 realworld 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 highperformance libraries and modern parallel hardware. Moreover, it avoids grammar growth which provides the possibility for queries optimization.
 GRADESNDA '18 Proceedings of the 1st ACM SIGMOD Joint International Workshop on Graph Data Management Experiences & Systems (GRADES) and Network Data Analytics (NDA),

Contextfree path querying (CFPQ) widely used for graphstructured data analysis in different areas. It is crucial to develop highly efficient algorithms for CFPQ since the size of the input data is typically large. We show how to reduce GFPQ evaluation to solving systems of matrix equations over R  a problem for which there exist highperformance solutions. Also, we demonstrate the applicability of our approach to realworld data analysis.Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data,

GRADESNDA'20: Proceedings of the 3rd Joint International Workshop on Graph Data Management Experiences & Systems (GRADES) and Network Data Analytics (NDA),
A recent study showed that the applicability of contextfree 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 matrixbased CFPQ algorithm by using appropriate highperformance libraries for linear algebra and integrate it with RedisGraph graph database. Also, we introduce a new CFPQ algorithm with singlepath 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 matrixbased CFPQ implementation for RedisGraph database is performant enough for realworld data analysis.

Proceedings of the 13th Central & Eastern European Software Engineering Conference in Russia (CEESECR '17),
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 contextfree 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.
 ICARCV,