Biography

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
  • GPGPU

Projects

Publications

  • Rustam Azimov, Semyon Grigorev

    The generalization of matrix-based Valiant's context-free language recognition algorithm for graph case is widely considered as a recipe for efficient context-free path querying; however, no progress has been made in this direction so far. We propose the first generalization of matrix-based Valiant's algorithm for context-free path querying. Our generalization does not deliver a truly sub-cubic worst-case complexity algorithm, whose existence still remains a hard open problem in the area. On the other hand, the utilization of matrix operations (such as matrix multiplication) in the process of context-free path query evaluation makes it possible to efficiently apply a wide class of optimizations and computing techniques, such as GPGPU, parallel processing, sparse matrix representation, distributed-memory computation, etc. Indeed, the evaluation on a set of conventional benchmarks shows, that our algorithm outperforms the existing ones.

    GRADES-NDA '18 Proceedings of the 1st ACM SIGMOD Joint International Workshop on Graph Data Management Experiences & Systems (GRADES) and Network Data Analytics (NDA), June 2018
  • 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