Graduated in 2016 from the Saint-Petersburg State University, Mathematics & Mechanics Faculty, Software Engineering Department with a bachelor's degree.

Currently, Rustam is a master’s student at SPbU, Mathematics & Mechanics Faculty.

Professional Activity

  • Graph parsing
  • Functional programming



  • Rustam Azimov, Semyon Grigorev

    Graph data model is widely used in many areas, for example, bioinformatics, graph databases, RDF. One of the most common graph queries are navigational queries. The result of query evaluation are implicit relations between nodes of the graph, i.e. paths in the graph. A natural way to specify these relations is by specifying paths using formal grammars over edge labels. This type of queries is usually evaluated using the relational query semantics. There is a number of algorithms for query evaluation which use such semantics but they have computational problems with big data. One of the most common technique for efficient big data processing is GPGPU, but these algorithms do not allow to use this technique effectively. In this paper we propose a graph parsing algorithm for query evaluation which use relational query semantics and context-free grammars, and is based on matrix operations which allows to speed up computations by means of GPGPU.

    arXiv, July 2017