大数据与物联网技术相互依存的特点

D. Ivanov, V. Alekseienko, T. Yarmolenko
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摘要

这项研究的一个主要方面是使用大数据工具来分析和有效利用物联网(IoT)设备产生的数据。大数据允许您存储和分析大量不同的数据,并从中获取有价值的信息。在大数据工具的帮助下,可以使用并行算法和分析方法实时或准实时地有效处理这些数据。本文的目的是分析大数据和物联网领域的最新相关研究,揭示它们相互依存的特点。为了实现这一目标,采用了科学文献系统综述的方法,系统地追踪和分析了该领域的重要研究和出版物。本研究的方法基于对文献来源和科学出版物的回顾性分析,这些文献和出版物与大数据和物联网在各个领域的使用有关。为了收集数据,系统地搜索了电子数据库,如IEEE Xplore, ScienceDirect,并搜索了网络资源,如Google Scholar。分析确定了大数据与物联网结合的关键概念、方法和实际应用。分析了物联网架构和大数据模型的最新发展。本研究的现实意义在于,有助于更深入地了解大数据与物联网领域的最新研究成果,以及两者之间的相互作用,并发现在各个行业中使用这种结合的新机会。这项研究的结果可用于开发新技术和改进现有的收集、处理和分析数据的系统。此外,这些发现可以作为在这一领域进一步研究的基础,以改进和实施新的解决办法。本研究有助于加深对大数据与物联网结合的潜力的理解,并指出该领域进一步发展的途径
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Features of the interdependence of Big Data and Internet of Things technologies
One of the main aspects of this study was the use of Big Data tools to analyze and effectively use the data generated by Internet of Things (IoT) devices. Big data allows you to store and analyze a huge amount of diverse data, as well as to obtain valuable information from it. With the help of Big Data tools, it is possible to process this data efficiently using parallel algorithms and analysis methods in real or quasi-real time. The purpose of the article is to analyze the latest relevant research in the field of Big Data and the Internet of Things, as well as to reveal the features of their interdependence. To achieve this goal, the method of systematic review of scientific literature was used, which allowed to systematically trace and analyze significant research and publications in this area. The methodology of this research is based on a retrospective analysis of literature sources and scientific publications related to the use of Big Data and the Internet of Things in various fields. To collect data, a systematic search of electronic databases such as IEEE Xplore, ScienceDirect, and searches on web resources such as Google Scholar were used. The analysis identified key concepts, methods, and practical applications of combining Big Data and the Internet of Things. The latest developments in the Internet of Things architecture and Big Data models were analyzed. The practical significance of the study is that it contributes to a deeper understanding of the latest research in the areas of Big Data and the Internet of Things, the interaction between them and the identification of new opportunities for using this combination in various industries. The results of this research can be used to develop new technologies and improve existing systems for collecting, processing, and analyzing data. In addition, these findings can be used as a basis for further research in this area aimed at improving and implementing new solutions. This study helps to deepen the understanding of the potential of combining Big Data and the Internet of Things and to point out ways for further development in this area
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