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• INVESTIGATION OF APPLICABILITY OF MULTIMODEL DATA WAREHOUSES IN GAMING INDUSTRY

  А.А. Koblov , О.М. Romakina , А.S. Klemesheva , А. Z. Arseneva

  105-121

  2025-12-30

  Abstract ▼

  This paper examines the feasibility and effectiveness of using multi-model databases for storing and processing data in the gaming industry. Modern gaming projects are characterized by highly complex and heterogeneous data: from strictly structured information about players, items, and quests to semi-structured and tightly coupled data, such as recipe systems, dialog trees, clan relationships, and in-game encyclopedias. Existing approaches based on relational or single-model NoSQL storage systems often fail to provide the necessary flexibility, performance, and development ease for such complex scenarios. The aim of this study is to design and comparatively analyze the performance of a multi-model solution for typical gaming mechanics. The authors developed a multi-model storage structure based on the ArangoDB DBMS that integrates document, graph, and key-value data models. The solution architecture encompasses key RPG game components: player and inventory management, quest systems, dialogue, crafting recipes, loot tables, clan relationships, and full-text search of the in-game encyclopedia using ArangoSearch. The experimental section includes a detailed performance comparison of the developed multi-model storage system with the PostgreSQL relational DBMS and the MongoDB document DBMS on realistic datasets and queries. The results demonstrate a significant advantage of the multi-model approach when performing operations that require traversing complex relationships: for example, searching for hostile players through a clan relationship graph in ArangoDB is, on average, 11 times faster than a similar JOIN query in PostgreSQL. However, for scenarios with frequent modifications to linearly organized data (e.g., updating quest status), the multi-model storage system exhibits slightly lower performance compared to the relational model, which, however, is acceptable within the context of the overall game project architecture. The study confirms that multi-model DBMSs, particularly ArangoDB, represent a promising solution for the gaming industry, enabling efficient combination of different data models within a single platform, simplifying development, and achieving high performance on complex data, which is critical for modern multiplayer games.

• DETERMINING THE NATURE OF PARAMETER CHANGES BASED ON THE ANALYSIS OF DYNAMICS RELATIVE TO THE SHAPE OF ITS VALUES SET IN REAL TIME

  S. I. Klevtsov

  2020-10-11

  Abstract ▼

  One of the important tasks of monitoring technical objects is the prevention of emergency
  situations. This task is associated with the implementation of a reliable and adequate assessment
  of the health of the object. The assessment of the object’s health is based on an analysis of the
  behavior of its controlled parameters in real time. Only then it will be relevant. A method for determining
  the nature of a parameter change based on an analysis of a sequence of special spatial
  graphical forms called Poincare graphs is proposed. The selected parameter should largely determine
  the operability of the controlled object. Charts are formed on the basis of the time series
  of the controlled parameter. A time window is selected that cuts the specified number of parameter
  values. A graph is plotted for each step of moving the window along the time series of the parameter.
  The transformation of the form of a given type is analyzed, which is superimposed on the totality of
  parameter values presented in the form of a graph. By changing the form parameters, a conclusion is
  drawn on the nature of the parameter changes. The paper shows the possibility of using Poincare
  graphs to track changes in the state of a technical object in real time. This takes into account the
  peculiarities of information retrieval from sensors. The assessment is implemented using a microprocessor
  module included in the monitoring system. The structure of a generalized one-factor model is
  also proposed, which tracks the change in the state of an object based on an analysis of Poincare
  graphs. The option of assessing the state of the object by comparing the characteristics of the graph
  with the criteria is given. The criteria are obtained after preliminary processing of a large array of
  data on the behavior of the controlled parameter. Each criterion value is associated with an expert
  assessment that determines the state of the object. The assessment allows you to determine the degree
  of operability of the facility and implement the necessary actions in case of danger.

• ON CALCULATING THE MEAN INFECTED TIME USING A DISCRETE MARKOV EPIDEMIOLOGICAL MODEL WITHOUT TREATMENT

  А.А. Magazev , А. Y. Nikiforova

  53-63

  2025-11-10

  Abstract ▼

  Modeling of the spread of viruses is a relevant research field. There are a lot of «continuous» epidemic models based on the use of systems of differential equations. The disadvantage of such models lies in their error in describing the initial stage of virus propagation and in the fact that they ignore the specific features of inter-individual connections. «Discrete» models, in which the time and the number of infected and susceptible nodes are discrete values, provide a more accurate picture of the epidemic process. In this work, we study a discrete Markov model in the case when there is no treatment. This is an important case, since it can be viewed as either an approximation to the initial phase of an epidemic or as a model for epidemics of viruses that are difficult to treat. The first section provides a detailed description of the properties of the Markov model used in this study. In the second section, using Markov approach, we define the mean infected time, i.e. the number of time steps taken to infect all individuals in the population. However, calculating the mean infected time in populations with a large number of individuals (or in networks with a large number of nodes) is computationally difficult problem, so in the third section we propose the corresponding approximate formula for this parameter. This approximation is designed for conditions of low network connectivity and а low probability of virus spread. In the fourth section, to validate our approximate formula, we compare its results against both exact calculations (using the fundamental matrix M) and data from simulation modeling. For the simulations, we developed a custom C++ console application. Our analysis demonstrates that all three methods yield consistent results under the specified conditions, confirming the practical utility of the simpler approximate formula