回顾大网络数据商业分析的数学模型和信息技术

Maliienko Stanislav, Selivorstova Tatyana
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引用次数: 0

摘要

本文全面回顾了用于分析web应用程序中大量数据的数学模型和信息技术。分析了该领域的最新研究和出版物,包括对机器学习方法、文本、图像、视频分析、社会网络分析和图算法的比较分析。本研究的目的是分析数学模型和信息技术在大型网络数据商业分析中的有效性和适用性。本文介绍了研究结果,并对各种方法的效率进行了比较分析,这将有助于业务分析人员选择最优的工具来处理和分析web应用程序中的大量数据。本文首先概述了该问题以及该领域的最新研究和出版物。本文提供了各种数学模型和信息技术的详细描述,包括它们的优缺点。对这些方法进行了比较分析,重点讨论了它们在商业分析中的有效性和适用性。文章还详细描述了数学模型和信息技术在各个行业的应用,如电子商务和供应链管理。本文分析了与在业务分析中使用这些技术相关的挑战和机遇,并为希望利用这些技术的企业提供了建议。总的来说,本文提供了一个全面的概述数学模型和信息技术用于大型网络数据的业务分析。对于希望了解该领域最新发展的业务分析师、数据科学家和研究人员来说,这篇文章是一份有价值的资源。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Review of mathematical models and information technologies for business analysis of the big web data
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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