Publications

Regular expressions are used in SPARQL property paths to query RDF graphs. However, regular expressions can only define the most limited class of languages, called regular languages. Contextfree languages are a wider class containing all regular languages. There are no contextfree expressions to define them, so it is necessary to write grammars. We propose an extension of regular expressions, called recursive expressions, to support the definition of a subset of contextfree languages. The goal of our work is therefore to provide simple operators allowing the definition of languages as close as possible to contextfree languages.ADBIS, TPDL and EDA 2020 Common Workshops and Doctoral Consortium,

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.
 The European Conference on ObjectOriented Programming (ECOOP),
 41st ACM SIGPLAN Conference on Programming Language Design and Implementation (PLDI 2020),
 41st ACM SIGPLAN Conference on Programming Language Design and Implementation (PLDI 2020),

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 Institute for System Programming,
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 stringmatching problem.

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,

PPoPP '20: Proceedings of the 25th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming,
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 precomputed, 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.

Programming and Computer Software,
Path querying with conjunctive grammars is known to be undecidable. There is an algorithm for path querying with linear conjunctive grammars which provides an overapproximation 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 matrixbased and allows us to efficiently apply GPGPU computing techniques and other optimizations for matrix operations.