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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.

• REALTIME NEURAL NETWORK ALGORITHM FOR FULL-FRAME MARINE SURFACE OBJECTS RECOGNITION

  V.A. Tupikov, V.A. Pavlova, V.A. Bondarenko, N.G. Holod

  2020-07-10

  Abstract ▼

  The article explores modern neural network architectures for the automatic detection and recognition of marine surface objects and obstacles of given classes throughout the full image area, applicable for execution in real or near real time on an optoelectronic vision system to au-tomate and improve the safety of civil marine navigation. A formal statement of the problem of automatic detection of objects on images is given. The state-of-the-art algorithms for detecting objects in images based on use of artificial convolutional neural networks were reviewed, their comparison was made and a reasonable choice was made in favor of the most efficient neuralnetwork architecture in terms of computational complexity to recognition accuracy. The subject area is studied, as well as publicly available databases of surface objects suitable for use in the training of algorithms using artificial neural networks. The article concluded that there is insuffi-cient labeled data for training neural network algorithms, as a result of which the authors inde-pendently collected research images and video sequences, prepared and labeled the collected data containing surface marine objects and other obstacles that represent a navigation hazard for ships. Based on the selected neural network architecture, a new neural network algorithm for automatic full-frame detection and recognition of surface objects was developed, and an artificial neural network was trained using the prepared database of images of typical objects. The resulting algorithm was tested by the authors on a validation data set, the quality of its work was estimated using various metrics, and the algorithm’s performance was measured. Conclusions are made about the necessity to expand the collected database of images of typical marine objects, further steps are proposed to improve the accuracy of the developed software and algorithmic complex and its implementation to be used in a marine optoelectronic machine vision system for automa-tion and improving the safety of civil navigation.

• METHOD FOR SEARCHING SEQUENTIAL PATTERNS OF USER'S BEHAVIOR ON THE INTERNET

  V.V. Kureychik, V. V. Bova, Y.A. Kravchenko

  2020-11-22

  Abstract ▼

  One of the important tasks of data mining is to isolate patterns and detect related events in
  sequential data based on the analysis of sequential patterns. The article examines the possibility of
  using sequential patterns to analyze the events of search and cognitive activity of users when interacting
  with Internet resources of an open information and educational environment. Searching
  for sequential patterns is a complex computational task whose goal is to retrieve all frequent sequences
  representing potential relationships within elements from a transactional database of
  sequences of search activity events with a given minimum support. To solve it, the article proposes
  a method for searching for patterns in sequences of events to detect hidden patterns that indicate
  possible levels of vulnerability when performing information search tasks in the Internet space.
  A mathematical model of user behavior in a search session based on the theory of sequential patterns
  is described. To improve the computational efficiency of the method, a modified algorithm
  for generating sequential patterns has been developed, at the first stage of which AprioriAll is
  performed, which forms frequent candidate sequences of all possible lengths, and at the second
  stage, a genetic algorithm for optimizing the input parameters of the feature space of the generated
  set to search for maximum patterns. A series of computational experiments were carried out on
  test data from the MSNBC corpus, the SPMF open source data mining library. The comparative
  analysis was carried out with the VMSP and GSP algorithms. The research results confirmed the
  efficiency of the search for maximum sequential patterns by the proposed algorithm in terms of the
  execution time and the number of extracted patterns. The results of the experimental studies of the
  method showed that to increase the stability and accuracy of the work, the sample size obtained as
  a result of the GA operation will reduce the required number of scans of the pattern database,
  providing acceptable computational costs comparable to the VMSP algorithm and the GSP algorithm
  that exceeds the search time for sequential patterns. an average of more than 150 %.

• DESIGN OF THE CONDITION DATABASE FOR ONLINE AND OFFLINE DATA PROCESSING IN EXPERIMENTAL SETUPS OF THE NICA COMPLEX

  K.V. Gertsenberger, A.I. Chebotov, I.N. Alexandrov, I.A. Filozova, E.I. Alexandrov

  2021-02-25

  Abstract ▼

  Storing, processing and analyzing of experimental and simulated data are an integral part of
  all modern high-energy physics experiments. These tasks are of particular importance in the experiments
  of the NICA project at the Joint Institute for Nuclear Research (JINR) due to the high interaction
  rate and particle multiplicity of ion collision events, therefore the task of automating the considered processes for the NICA complex has particular relevance. To solve the task, modern physics
  experiments use various information systems, which control experiment data flows and simultaneously
  service a large number of requests from various systems and collaboration members. The article
  describes the design of a new information system based on the Condition Database as well as related
  information services to automate storing and processing of data and information on the experiments.
  The Condition Database is aimed at storing, searching and using various parameters and operation
  modes of experiment systems. The system being implemented on the PostgreSQL DBMS will provide
  the information for event data processing and physics analysis and organize a transparent, unified
  access and data management throughout the life cycle of the scientific research. The article shows
  the scheme and purposes of the Condition Database and its attributes, key aspects of the design are
  highlighted. A place of the Condition Database in data processing flow is illustrated. The integration
  of the information system with experiment software systems is also presented. The development of the
  Condition Database interfaces has been started to use the stored information in event simulation, raw
  data processing, reconstruction and physics analysis tasks.

• MODERN AVAILABLE PALMPRINT DATABASES: A REVIEW

  Snehal S. Datwase, R.R. Deshmukh, Rohit S. Gupta

  27-37

  2025-07-31

  Abstract ▼

  The palm print is a unique and very useful biometric. A lot of research has been done on this
  topic over the past few decades. Various algorithms and systems have been developed and successfully
  implemented. Since this method does not provide more advanced information for personality
  recognition, multispectral or hyperspectral imaging and handprint recognition could be a
  potential answer to these systems. Biometric technologies have been widely used in the security
  industry for authentication and identification over the past few years. An improved recognition
  system is required to improve accuracy and speed. This article reviews some modern handprint
  databases and describes the methods used and their accuracy. Face, fingerprint, iris, palm print,
  hands are physiological biometric data. Of all biometrics, physiological biometrics offers the most
  benefits. The PolyU-IITD non-contact palm image database compiled with a handheld camera
  includes residents of India and China. The database of IIT Touchless Palmprints is sourced from
  Delhi India students and teachers and consists of complete hand images. The database of
  hyperspectral fingerprints created by the Hong Kong Polytechnic University was collected in the Biometric Research Laboratory Department using Meadowlark liquid crystal filters. The Multispectral
  Fingerprint Database, Hyperspectral Database was compiled by Chinese research teams
  of scientists. The polyU fingerprint database was collected from 193 people and contains
  386 palms. The Chinese Academy of Sciences has developed the CASIA handprint database with
  its own handprint recognition device. The XJTU fingerprint database is collected using iPhone 6S,
  HUAWEI mate8, LG G4, Samsung Galaxy Note5 and MI8 gadgets. A literature review of current
  research in this area is also presented. The advantages of hyperspectral images compared to multispectral
  images are noted, hyperspectral images of palm prints are very difficult to fake