Review of mathematical models and information technologies for business analysis of the big web data
DOI:
https://doi.org/10.34185/1562-9945-4-147-2023-02Keywords:
mathematical models, information technologies, business analysis, web data, machine learning, social network analysis, graph algorithms, big data analyticsAbstract
The article provides a comprehensive review of mathematical models and information technologies used for analyzing large amounts of data in web applications. The latest re-search and publications in the field are analyzed, including a comparative analysis of ma-chine learning methods, text, image, video analysis, social network analysis, and graph algo-rithms. The goal of this research is to analyze the effectiveness and applicability of mathe-matical models and information technologies in business analysis of large web data. The arti-cle presents the results of the research and a comparative analysis of the efficiency of meth-ods, which will help business analysts choose the optimal tools for processing and analyzing large amounts of data in web applications. The article begins with an overview of the problem and the latest research and publica-tions in the field. The article provides a detailed description of various mathematical models and information technologies, including their strengths and weaknesses. A comparative analysis of these methods is presented, with a focus on their effectiveness and applicability in business analysis. The article also provides a detailed description of the applications of mathematical models and information technologies in various industries, such as e-commerce and supply chain management. The article analyzes the challenges and opportunities associated with the use of these technologies in business analysis and provides recommendations for businesses that want to take advantage of these technologies. Overall, the article provides a comprehensive overview of mathematical models and in-formation technologies used in business analysis of large web data. The article is a valuable resource for business analysts, data scientists, and researchers who want to learn more about the latest developments in this field.
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