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 METHODOLOGICAL DETAILING OF THE PROCESS FOR MODELING THE PROPERTIES OF CONTENT-CENTRIC MESSAGES ENCODED IN THE FORM OF SYMBOLIC DATA CONSTRUCTS
Tác giả hoặc Nhóm tác giả: KONNIKOV E.A.1, TRAN H.H.1, RODIONOV D.G.1, ZAYTSEV A.A.1
Nơi đăng: ЭКОНОМИЧЕСКИЙ ВЕСТНИК Учредители: ИП Клюев Сергей Васильевич eISSN: 2949-4648; Số: 2;Từ->đến trang: 8-18;Năm: 2024
Lĩnh vực: Công nghệ thông tin; Loại: Bài báo khoa học; Thể loại: Quốc tế
TÓM TẮT
ABSTRACT
The purpose of the study is to develop mathematical models for encoding the properties of content-centric messages within data constructs, particularly focusing on the universal coding of input arrays to ensure comparability in symbolic forms. This approach is pivotal for bridging natural and symbolic information, facilitating deeper analytical insights into data structures in information systems.Methods: The methodology employed involves an integrated approach using natural text forms and symbolic information parsing techniques. These methods encompass both automated and manual data collection processes, aimed at forming a structured array of symbolic data constructs. Key techniques include tokenization, lemmatization, and vectorization, which help in transforming natural text into a format suitable for analytical models and machine learning algorithms.Findings: The research has successfully developed models that quantify and analyze the intensity and frequency of thematic entities within the information backdrop. These models are based on a combination of gamma and multivariate normal distributions, providing a comprehensive analytical view of the data structure. The findings enable not only the description but also the prediction of the characteristics of information flows, thus opening new avenues for analyzing and planning information policies. Conclusions : The developed mathematical models and methodologies provide powerful tools for more effective planning and decision-making in information management. By applying logical and algorithmic techniques to analyze symbolic and natural information, these models facilitate a deeper understanding of data structures, enabling enterprises to optimize their strategic interactions within the information environment. Furthermore, the predictive capabilities of these models allow for forecasting the dynamics of information flows, thereby playing a crucial role in shaping information policies and corporate strategies.
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