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• SEMANTIC ANALYSIS AND INTEGRATION OF HETEROGENEOUS INFORMATION STREAMS IN DECISION SUPPORT SYSTEMS: A TECHNOLOGY REVIEW

  V. V. Gapochka , Е. Е. Polupanova

  2026-02-27

  Abstract ▼

  Modern decision support systems (DSS) increasingly rely on heterogeneous data streams from IoT sensors, databases, text messages, and social media, represented in different formats and characterized by diverse semantic models and quality levels. The lack of semantically aligned integration results in inconsistent entity interpretation, duplication, and loss of context, which reduces the quality and timeliness of decisions. The aim of this paper is to systematize methods for semantic analysis and integration of heterogeneous information streams in DSS and to identify their benefits, limitations, and application domains. The study is conducted as an analytical review of publications from 2018–2025 focusing on semantic interoperability, ontologies and knowledge graphs, multi-source data fusion, data federation, and real-time stream processing. The review shows that semantic compatibility is primarily achieved through ontologies and knowledge graphs that define shared entities and identifiers and provide a flexible integration schema. For real-time decision-making, hybrid solutions combining a semantic layer with data fusion algorithms and source trust assessment are the most effective; published case studies report accuracy gains of about 15–20% and response-time reductions of up to 70–80% in multi-source settings. For unstructured streams, NLP and machine learning play a key role by extracting entities and relations and enabling semantic enrichment. The results can be used to design DSS for smart city, industrial, and healthcare domains. Furthermore, the paper highlights the role of standards like SHACL for validation and SPARQL for querying, enhancing the practical applicability of semantic approaches. Future directions include automating ontology alignment to reduce labor costs and integrating with AI for dynamic adaptation to new data sources.

• SEQUENTIAL HYBRIDIZATION ALGORITHM FOR THE TRAVELING SALESMAN PROBLEM SOLVING

  Е. Е., А.А. Rybalko

  2023-08-14

  Abstract ▼

  The traveling salesman problem is a combinatorial optimization problem. The article presents
  a statement of this problem and proposes a graph mathematical model in which vertices
  correspond to cities, and edges are paths between cities, and it is assumed that the graph is
  weighted. The solution of the traveling salesman problem consists in finding the minimum weight
  Hamiltonian cycle in a complete weighted graph. The problem is NP-hard, so a heuristic approach
  is used to solve this problem and speed up the solution of the problem on large volumes of
  input data. The heuristic consists in applying hybridization of two algorithms to solve the traveling
  salesman problem: the annealing simulation algorithm and the nearest neighbor algorithm. Sequential
  hybridization scheme is used to solve the traveling salesman problem. The basic idea is
  that the nearest neighbor method is launched on the initial set of solutions, and then the best solution
  of the first stage is fed to the annealing simulation algorithm. The article details the construction,
  flowcharts of the hybrid algorithm, the annealing simulation algorithm, and the nearest
  neighbor method. The article goes on to describe the user interface of the application written in
  Typescript. The application uses an area map as a solution to the traveling salesman problem. In
  the last part of the article, a comparative analysis of the algorithms' performance is highlighted: a
  comparison of the accuracy and operating time of the developed hybrid algorithm, the annealing simulation algorithm, and the nearest neighbor method for different input data sets. It was established
  that the developed hybrid algorithm is in second place in terms of speed and in first place in
  terms of solution quality among the implemented algorithms. In addition, the developed solution
  has a high economic and practical value because an application for solving the traveling salesman
  problem, and therefore an application for route navigation, can replace existing analogues or it
  can be used in any narrowly focused areas, as well as in logistics.

• COMPILING A DIET BASED ON A GENETIC ALGORITHM

  Е.Е. Polupanova, А. S. Oleynik

  2023-08-14

  Abstract ▼

  This work is devoted to solving the problem of compiling a diet using a genetic algorithm.
  The task of compiling a diet is a combinatorial optimization problem. The main purpose of solving
  the problem of compiling a diet is to find a suitable combination of dishes to perform the distribution
  in accordance with the special needs of a person. The article provides a statement of compiling
  a diet problem and its mathematical model. Since the task of compiling a diet is NP-hard and
  the input data may require large computational costs for an accurate algorithm, it is reasonable to
  apply a heuristic approach to solving this problem. The article highlights are in detail the main
  concepts of the theory of genetic algorithms, the sequence of steps of the developed genetic algorithm
  for compiling the diet, the flowchart of the genetic algorithm. To research the genetic algorithm
  of compiling a diet there was developed a client-server application running the Android
  operating system. The result of the genetic algorithm for compiling a diet is the seven days menu,
  which is displayed and stored in the application. The client-server architecture of the application
  was chosen in order to save the user's phone resources. The description of the Android-application user interface with the ability to adjust various parameters of the algorithm is given in the article.
  Also the analysis of the obtained algorithm efficiency is highlighted: an estimation of the accuracy
  and operating time of the developed genetic algorithm with different configurations of the algorithm.
  Based on the results of the experiments, it was possible to determine the optimal values of
  the configurable parameters of the genetic algorithm (the number of chromosomes, the number of
  iterations, the probability of mutation), allowing to obtain good results in an acceptable time.
  The characteristic features of the implemented genetic algorithm of compiling a diet is a relatively
  short operating time, even in a large input data. In addition, the developed solution has a high
  economic value due to the application of the algorithm in practice, for example, in the work of
  nutritionists, fitness trainers, as well as for ordinary overweight users.

• HEURISTIC GENETIC ALGORITHM FOR DIOPHANTINE EQUATIONS SOLVING

  Е.Е. Polupanova, P.E. Usov

  115-123

  2022-01-31

  Abstract ▼

  The problem of diophantine equations solving is considered in this article. This problem can
  be applied in cryptography and cryptanalysis. The description of the genetic algorithm solving
  diophantine equations is stated briefly in the article. The rule of calculation the value of fitness
  function of chromosome is determined, the coding system in the genetic algorithm is described.
  The genetic operators used in the algorithm are mentioned and the conditions for their execution
  are determined. The criterion for stopping the genetic algorithm is described. One of the shortcomings
  of the genetic algorithm is analyzed. The shortcoming of the algorithm lies in its attempts
  to solve any diophantine equation, including one that has no solutions. A method eliminating this
  shortcoming in some cases is proposed. This method is based on number theory. An explanation is
  given in which cases this method will be used. The definition of residue and nonresidue of fixed
  power for fixed modulus is given before describing this method. After describing this method the
  implementation of the algorithm for solving diophantine equations and systems of them is described
  in detail. Then the results of experimental studies of the time and quality of the genetic
  algorithm are presented. Then the result of the algorithm is presented for an equation that has no
  solutions and for a system of equations that also has no solutions, but in which the total number of
  unknowns is too large for the proposed method to work. The algorithm running time is compared
  when solving an equation and when solving a system of equations. The conclusion is made about
  the usefulness of the proposed method in solving diophantine equations and systems of diophantine
  equations.