<?xml version="1.0" encoding="UTF-8"?>
<!DOCTYPE root>
<article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:ali="http://www.niso.org/schemas/ali/1.0/" article-type="research-article" dtd-version="1.2" xml:lang="en"><front><journal-meta><journal-id journal-id-type="publisher-id">Computational nanotechnology</journal-id><journal-title-group><journal-title xml:lang="en">Computational nanotechnology</journal-title><trans-title-group xml:lang="kk"><trans-title>Computational nanotechnology</trans-title></trans-title-group><trans-title-group xml:lang="pt"><trans-title>Computational nanotechnology</trans-title></trans-title-group><trans-title-group xml:lang="ru"><trans-title>Computational nanotechnology</trans-title></trans-title-group><trans-title-group xml:lang="zh"><trans-title>Computational nanotechnology</trans-title></trans-title-group></journal-title-group><issn publication-format="print">2313-223X</issn><issn publication-format="electronic">2587-9693</issn><publisher><publisher-name xml:lang="en">YUR-VAK</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="publisher-id">689213</article-id><article-id pub-id-type="doi">10.33693/2313-223X-2025-12-2-58-67</article-id><article-id pub-id-type="edn">QIICSF</article-id><article-categories><subj-group subj-group-type="toc-heading" xml:lang="en"><subject>MATHEMATICAL AND SOFTWARE OF COMPUTЕRS,  COMPLEXES AND COMPUTER NETWORKS</subject></subj-group><subj-group subj-group-type="toc-heading" xml:lang="ru"><subject>МАТЕМАТИЧЕСКОЕ И ПРОГРАММНОЕ ОБЕСПЕЧЕНИЕ  ВЫЧИСЛИТЕЛЬНЫХ СИСТЕМ, КОМПЛЕКСОВ  И КОМПЬЮТЕРНЫХ СЕТЕЙ</subject></subj-group><subj-group subj-group-type="article-type"><subject>Research Article</subject></subj-group></article-categories><title-group><article-title xml:lang="en">Effective data model selection for infological entities in multimodel database systems</article-title><trans-title-group xml:lang="ru"><trans-title>Выбор эффективных моделей данных инфологических сущностей в мультимодельных базах данных</trans-title></trans-title-group></title-group><contrib-group><contrib contrib-type="author"><contrib-id contrib-id-type="orcid">https://orcid.org/0009-0008-3076-8076</contrib-id><contrib-id contrib-id-type="spin">2572-3667</contrib-id><name-alternatives><name xml:lang="en"><surname>Mishin</surname><given-names>Nikita S.</given-names></name><name xml:lang="ru"><surname>Мишин</surname><given-names>Никита Сергеевич</given-names></name></name-alternatives><address><country country="RU">Russian Federation</country></address><bio xml:lang="en"><p>postgraduate student, Department of Information Processing and Control Systems</p></bio><bio xml:lang="ru"><p>аспирант, кафедра систем обработки информации и управления</p></bio><email>stancuem@yandex.ru</email><xref ref-type="aff" rid="aff1"/></contrib><contrib contrib-type="author"><name-alternatives><name xml:lang="en"><surname>Afanasyev</surname><given-names>Gennady I.</given-names></name><name xml:lang="ru"><surname>Афанасьев</surname><given-names>Геннадий Иванович</given-names></name></name-alternatives><address><country country="RU">Russian Federation</country></address><bio xml:lang="en"><p>Cand. Sci. (Eng.), Associate Professor; associate professor</p></bio><bio xml:lang="ru"><p>кандидат технических наук, доцент; доцент</p></bio><email>gaipcs@bmstu.ru</email><xref ref-type="aff" rid="aff1"/></contrib><contrib contrib-type="author"><contrib-id contrib-id-type="orcid">https://orcid.org/0000-0002-0596-4955</contrib-id><contrib-id contrib-id-type="spin">6631-0932</contrib-id><name-alternatives><name xml:lang="en"><surname>Khayrullin</surname><given-names>Rustam Z.</given-names></name><name xml:lang="ru"><surname>Хайруллин</surname><given-names>Рустам Зиннатуллович</given-names></name></name-alternatives><address><country country="RU">Russian Federation</country></address><bio xml:lang="en"><p>Dr. Sci. (Eng.), Senior Scientific Worker; Professor</p></bio><bio xml:lang="ru"><p>доктор физико-математических наук, старший научный сотрудник; профессор</p></bio><email>zrkzrk@list.ru</email><xref ref-type="aff" rid="aff1"/><xref ref-type="aff" rid="aff2"/></contrib></contrib-group><aff-alternatives id="aff1"><aff><institution xml:lang="en">Bauman Moscow State Technical University</institution></aff><aff><institution xml:lang="ru">Московский государственный технический университет имени Н.Э. Баумана</institution></aff></aff-alternatives><aff-alternatives id="aff2"><aff><institution xml:lang="en">Moscow State University of Civil Engineering (National Research University)</institution></aff><aff><institution xml:lang="ru">Московский государственный строительный университет (национальный исследовательский университет)</institution></aff></aff-alternatives><pub-date date-type="pub" iso-8601-date="2025-08-19" publication-format="electronic"><day>19</day><month>08</month><year>2025</year></pub-date><volume>12</volume><issue>2</issue><issue-title xml:lang="en"/><issue-title xml:lang="ru"/><fpage>58</fpage><lpage>67</lpage><history><date date-type="received" iso-8601-date="2025-08-14"><day>14</day><month>08</month><year>2025</year></date><date date-type="accepted" iso-8601-date="2025-08-14"><day>14</day><month>08</month><year>2025</year></date></history><permissions><copyright-statement xml:lang="en">Copyright ©; 2025, Yur-VAK</copyright-statement><copyright-statement xml:lang="ru">Copyright ©; 2025, Юр-ВАК</copyright-statement><copyright-year>2025</copyright-year><copyright-holder xml:lang="en">Yur-VAK</copyright-holder><copyright-holder xml:lang="ru">Юр-ВАК</copyright-holder><ali:free_to_read xmlns:ali="http://www.niso.org/schemas/ali/1.0/" start_date="2026-08-19"/><license><ali:license_ref xmlns:ali="http://www.niso.org/schemas/ali/1.0/">https://journals.eco-vector.com/2313-223X/about/editorialPolicies</ali:license_ref></license></permissions><self-uri xlink:href="https://journals.eco-vector.com/2313-223X/article/view/689213">https://journals.eco-vector.com/2313-223X/article/view/689213</self-uri><abstract xml:lang="en"><p>The article addresses the problem of selecting effective data models for infological entities in the context of designing multimodel databases. The focus is placed on the need for a systematic approach when modeling heterogeneous entities whose structure and behavior require different forms of representation. The study analyzes the characteristics of three widely used models – relational, graph, and multidimensional – in terms of their applicability to various types of infological entities. Key criteria influencing model selection are described, including data structure, interconnectivity, query patterns, mutability, scalability, and consistency requirements. A decision-making algorithm is proposed, based on analyzing entity characteristics and the system’s non-functional requirements. Particular attention is given to the advantages and challenges of multimodel solutions, as well as principles of coordinating different models within a unified architectural framework. The work aims to provide a methodological foundation for rational model selection and for enhancing the adaptability and sustainability of information systems.</p></abstract><trans-abstract xml:lang="ru"><p>В статье рассматривается проблема выбора эффективных моделей данных для инфологических сущностей в условиях проектирования мультимодельных баз данных. Акцент сделан на необходимость системного подхода при моделировании разнородных сущностей, структура и поведение которых требуют различной формы представления. Исследуются особенности трех наиболее распространенных моделей – реляционной, графовой и многомерной, – с точки зрения их применимости к различным типам инфологических сущностей. Описаны ключевые критерии, влияющие на выбор модели данных: структура и связность сущностей, характер запросов, изменчивость данных, требования к масштабируемости и целостности. Представлен алгоритм принятия проектного решения на основе анализа характеристик сущности и нефункциональных требований системы. Особое внимание уделено возможностям и ограничениям мультимодельных решений, а также принципам координации разных моделей в едином архитектурном пространстве. Работа направлена на формирование методологической базы, обеспечивающей обоснованный выбор моделей данных и повышение устойчивости информационных систем к изменениям.</p></trans-abstract><kwd-group xml:lang="en"><kwd>infological entities</kwd><kwd>data models</kwd><kwd>multimodel databases</kwd><kwd>relational model</kwd><kwd>graph model</kwd><kwd>multidimensional model</kwd><kwd>database design</kwd></kwd-group><kwd-group xml:lang="ru"><kwd>инфологические сущности</kwd><kwd>модели данных</kwd><kwd>мультимодельные базы данных</kwd><kwd>реляционная модель</kwd><kwd>графовая модель</kwd><kwd>многомерная модель</kwd><kwd>проектирование баз данных</kwd></kwd-group><funding-group/></article-meta></front><body></body><back><ref-list><ref id="B1"><label>1.</label><mixed-citation>Aguilar Vera R. et al. NoSQL database modeling and management: A systematic literature review. Revista Facultad de Ingeniería. 2023. Vol. 32. No. 65 (32). Art. e16519. DOI: 10.19053/01211129.v32.n65.2023.16519.</mixed-citation></ref><ref id="B2"><label>2.</label><mixed-citation>Daniel G. et al. NeoEMF: A multi-database model persistence framework for very large models. Science of Computer Programming. 2017. No. 149. Pp. 9–14. DOI: 10.1016/j.scico.2017.08.002.</mixed-citation></ref><ref id="B3"><label>3.</label><mixed-citation>Gabrielsen R.H., Olesen O. The Concept of lineaments in geological structural analysis; Principles and methods: A review based on examples from Norway. Geomatics. 2024. No. 2 (4). Pp. 189–212. DOI: 10.3390/geomatics4020011.</mixed-citation></ref><ref id="B4"><label>4.</label><mixed-citation>Gerasimov V.R., Dusheba V.V. Analysis of optimizing database performance methods // Èlektronnoe modelirovanie. 2024. No. 6 (46). Pp. 43–54. DOI: 10.15407/emodel.46.06.043.</mixed-citation></ref><ref id="B5"><label>5.</label><mixed-citation>Gupta S., Pal S., Chakraborty M. A Study on various database models: Relational, graph, and hybrid databases. Singapore: Springer, 2019. Pp. 141–149. DOI: 10.1007/978-981-15-0361-0_11.</mixed-citation></ref><ref id="B6"><label>6.</label><mixed-citation>Jiri H. et al. Multidimensional database for crime prevention. IEEE, 2016. Pp. 242–247. DOI: 10.1109/carpathiancc.2016.7501102.</mixed-citation></ref><ref id="B7"><label>7.</label><mixed-citation>Labiadh M. et al. A microservice-based framework for exploring data selection in cross-building knowledge transfer. Service Oriented Computing and Applications. 2020. No. 2 (15). Pp. 97–107. DOI: 10.1007/s11761-020-00306-w.</mixed-citation></ref><ref id="B8"><label>8.</label><mixed-citation>Larson D., Chang V. A review and future direction of agile, business intelligence, analytics and data science. International Journal of Information Management. 2016. No. 5 (36). Pp. 700–710. DOI: 10.1016/j.ijinfomgt.2016.04.013.</mixed-citation></ref><ref id="B9"><label>9.</label><mixed-citation>Lebedev I.I., Ogorodnikov S.S. Storage and analysis of remote sensing data // Russian Engineering Research. 2024. No. 4 (44). Pp. 597–599. DOI: 10.3103/s1068798x24700485.</mixed-citation></ref><ref id="B10"><label>10.</label><mixed-citation>Lou J. et al. Willingness to pay for well-being housing attributes driven by design layout: Evidence from Hong Kong. Building and Environment. 2024. No. 251. Art. 111227. DOI: 10.1016/j.buildenv.2024.111227.</mixed-citation></ref><ref id="B11"><label>11.</label><mixed-citation>Margara A. et al. A model and survey of distributed data-intensive systems. ACM Computing Surveys. 2023. No. 1 (56). Pp. 1–69. DOI: 10.1145/3604801.</mixed-citation></ref><ref id="B12"><label>12.</label><mixed-citation>Molka-Danielsen J., Engelseth P., Wang H. Large scale integration of wireless sensor network technologies for air quality monitoring at a logistics shipping base. Journal of Industrial Information Integration. 2018. No. 10. Pp. 20–28. DOI: 10.1016/j.jii.2018.02.001.</mixed-citation></ref><ref id="B13"><label>13.</label><mixed-citation>Murri S. Optimising data modeling approaches for scalable data warehousing systems. International Journal of Scientific Research in Science, Engineering and Technology. 2023. Pp. 369–382. DOI: 10.32628/ijsrset2358716.</mixed-citation></ref><ref id="B14"><label>14.</label><mixed-citation>Pekaric I. et al. A systematic review on security and safety of self-adaptive systems. Journal of Systems and Software. 2023. No. 203. Art. 111716. DOI: 10.1016/j.jss.2023.111716.</mixed-citation></ref><ref id="B15"><label>15.</label><mixed-citation>Petkov Y.I., Chikalanov A.I. Innovative proposals for database storage and management. Mathematics and Informatics. 2022. No. 1 (LXV). Pp. 45–52. DOI: 10.53656/math2022-1-6-inn.</mixed-citation></ref><ref id="B16"><label>16.</label><mixed-citation>Sarawagi S. Models and indices for integrating unstructured data with a relational database. Berlin; Heidelberg: Springer, 2005. Pp. 1–10. DOI:10.1007/978-3-540-31841-5_1.</mixed-citation></ref><ref id="B17"><label>17.</label><mixed-citation>Shah K., Patel K.S. A Survey on relational database based multi relational classification algorithms. International Journal of Scientific Research in Computer Science, Engineering and Information Technology. 2024. No. 2 (10). Pp. 140–147. DOI: 10.32628/cseit2390656.</mixed-citation></ref><ref id="B18"><label>18.</label><mixed-citation>Shahidinejad J., Kalantari M., Rajabifard A. 3D cadastral database systems – a systematic literature review. ISPRS International Journal of Geo-Information. 2024. No. 1 (13). P. 30. DOI: 10.3390/ijgi13010030.</mixed-citation></ref><ref id="B19"><label>19.</label><mixed-citation>Tan Z., Yue P., Gong J. An array database approach for earth observation data management and processing. ISPRS International Journal of Geo-Information. 2017. No. 7 (6). P. 220. DOI: 10.3390/ijgi6070220.</mixed-citation></ref><ref id="B20"><label>20.</label><mixed-citation>Zhang J. et al. Public cloud networks oriented deep neural networks for effective intrusion detection in online music education. Computers and Electrical Engineering. 2024. No. 115. Art. 109095. DOI 10.1016/j.compeleceng.2024.109095.</mixed-citation></ref></ref-list></back></article>
