Methods of working with “big data” based on the application of “m-tuple” theory.

  • О. Syrotkina National Technical University "Dniprovsk Polytechnic"
  • M. Alekseyev National Technical University "Dniprovsk Polytechnic"
  • L. Meshcheriakov National Technical University "Dniprovsk Polytechnic"
  • В. Moroz National Technical University "Dniprovsk Polytechnic"
Keywords: “big data”, data organization structure, ordered sets of arbitrary cardinality, m-tuples, Boolean graph, estimation of algorithm execution time, minimization of time and computing resources.


This article addresses the creation and application of mathematical methods to work with “big data”. This allows us to minimize the time and computational resources involved for a data organization structure “m-tuples based on ordered sets of arbitrary cardinality (OSAC)”. We reviewed and analysed mathematical methods used to solve problems of this class. We formulated several properties of this data organization structure which are a consequence of the logical rules for the formation of this structure. A set of functional dependencies between m-tuples is derived based on their location in the structure and is determined by a pair of indices (j, m) under particular initial conditions. It is also given the illustration of the Boolean graph. The outlined vertices of the graph are determined using the derived analytical dependencies as Boolean elements that include the same general element under particular initial conditions. We made a comparative evaluation between the execution time for the method to work with “big data” and the analogue methods described in the article. We obtained logical conclusions about the influence of the properties studied and methods to work with the structure “m-tuples based on OSAC” on the minimization of the computing resources involved.


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How to Cite
SyrotkinaО., Alekseyev, M., Meshcheriakov, L., & MorozВ. (2019). Methods of working with “big data” based on the application of “m-tuple” theory. COMPUTER-INTEGRATED TECHNOLOGIES: EDUCATION, SCIENCE, PRODUCTION, (36), 140-152.
Computer science and computer engineering