Intersections of Big Data and IoT in Academic Publications: A Topic Modeling Approach
As vast amounts of data are generated from various sources such as social media, sensors and online transactions, the analysis of Big Data offers organizations the ability to derive insights and make informed decisions. Simultaneously, IoT connects physical devices, enabling real-time data collectio...
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MDPI AG
2025-02-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/3/906 |
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| author | Diana-Andreea Căuniac Andreea-Alexandra Cîrnaru Simona-Vasilica Oprea Adela Bâra |
| author_facet | Diana-Andreea Căuniac Andreea-Alexandra Cîrnaru Simona-Vasilica Oprea Adela Bâra |
| author_sort | Diana-Andreea Căuniac |
| collection | DOAJ |
| description | As vast amounts of data are generated from various sources such as social media, sensors and online transactions, the analysis of Big Data offers organizations the ability to derive insights and make informed decisions. Simultaneously, IoT connects physical devices, enabling real-time data collection and exchange that transforms interactions within smart homes, cities and industries. The intersection of these fields is essential, leading to innovations such as predictive maintenance, real-time traffic management and personalized solutions. Utilizing a dataset of 8159 publications sourced from the Web of Science database, our research employs Natural Language Processing (NLP) techniques and selective human validation to analyze abstracts, titles, keywords and other useful information, uncovering key themes and trends in both Big Data and IoT research. Six topics are extracted using Latent Dirichlet Allocation. In Topic 1, words like “system” and “energy” are among the most frequent, signaling that Topic 1 revolves around <i>data systems and IoT technologies</i>, likely in the context of smart systems and energy-related applications. Topic 2 focuses on the <i>application of technologies</i>, as indicated by terms such as “technologies”, “industry” and “research”. It deals with how IoT and related technologies are transforming various industries. Topic 3 emphasizes terms like learning and research, indicating a focus on <i>machine learning and IoT applications</i>. It is oriented toward research involving new methods and models in the IoT domain related to learning algorithms. Topic 4 highlights terms such as smart, suggesting a focus on <i>smart technologies and systems</i>. Topic 5 touches upon the role of digital chains and supply systems, suggesting an industrial focus on <i>digital transformation</i>. Topic 6 focuses on technical aspects such as <i>modeling, system performance and prediction algorithms</i>. It delves into the efficiency of IoT networks with terms like “accuracy”, “power” and “performance” standing out. |
| format | Article |
| id | doaj-art-b765b0e5791a476982490e1eb0334fac |
| institution | OA Journals |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-b765b0e5791a476982490e1eb0334fac2025-08-20T02:12:33ZengMDPI AGSensors1424-82202025-02-0125390610.3390/s25030906Intersections of Big Data and IoT in Academic Publications: A Topic Modeling ApproachDiana-Andreea Căuniac0Andreea-Alexandra Cîrnaru1Simona-Vasilica Oprea2Adela Bâra3Department of Economic Informatics and Cybernetics, Bucharest University of Economic Studies, No. 6 Piaţa Romană, 010374 Bucharest, RomaniaDepartment of Economic Informatics and Cybernetics, Bucharest University of Economic Studies, No. 6 Piaţa Romană, 010374 Bucharest, RomaniaDepartment of Economic Informatics and Cybernetics, Bucharest University of Economic Studies, No. 6 Piaţa Romană, 010374 Bucharest, RomaniaDepartment of Economic Informatics and Cybernetics, Bucharest University of Economic Studies, No. 6 Piaţa Romană, 010374 Bucharest, RomaniaAs vast amounts of data are generated from various sources such as social media, sensors and online transactions, the analysis of Big Data offers organizations the ability to derive insights and make informed decisions. Simultaneously, IoT connects physical devices, enabling real-time data collection and exchange that transforms interactions within smart homes, cities and industries. The intersection of these fields is essential, leading to innovations such as predictive maintenance, real-time traffic management and personalized solutions. Utilizing a dataset of 8159 publications sourced from the Web of Science database, our research employs Natural Language Processing (NLP) techniques and selective human validation to analyze abstracts, titles, keywords and other useful information, uncovering key themes and trends in both Big Data and IoT research. Six topics are extracted using Latent Dirichlet Allocation. In Topic 1, words like “system” and “energy” are among the most frequent, signaling that Topic 1 revolves around <i>data systems and IoT technologies</i>, likely in the context of smart systems and energy-related applications. Topic 2 focuses on the <i>application of technologies</i>, as indicated by terms such as “technologies”, “industry” and “research”. It deals with how IoT and related technologies are transforming various industries. Topic 3 emphasizes terms like learning and research, indicating a focus on <i>machine learning and IoT applications</i>. It is oriented toward research involving new methods and models in the IoT domain related to learning algorithms. Topic 4 highlights terms such as smart, suggesting a focus on <i>smart technologies and systems</i>. Topic 5 touches upon the role of digital chains and supply systems, suggesting an industrial focus on <i>digital transformation</i>. Topic 6 focuses on technical aspects such as <i>modeling, system performance and prediction algorithms</i>. It delves into the efficiency of IoT networks with terms like “accuracy”, “power” and “performance” standing out.https://www.mdpi.com/1424-8220/25/3/906IoTbig dataLatent Dirichlet Allocation (LDA)Natural Language Processing (NLP)networkdata |
| spellingShingle | Diana-Andreea Căuniac Andreea-Alexandra Cîrnaru Simona-Vasilica Oprea Adela Bâra Intersections of Big Data and IoT in Academic Publications: A Topic Modeling Approach Sensors IoT big data Latent Dirichlet Allocation (LDA) Natural Language Processing (NLP) network data |
| title | Intersections of Big Data and IoT in Academic Publications: A Topic Modeling Approach |
| title_full | Intersections of Big Data and IoT in Academic Publications: A Topic Modeling Approach |
| title_fullStr | Intersections of Big Data and IoT in Academic Publications: A Topic Modeling Approach |
| title_full_unstemmed | Intersections of Big Data and IoT in Academic Publications: A Topic Modeling Approach |
| title_short | Intersections of Big Data and IoT in Academic Publications: A Topic Modeling Approach |
| title_sort | intersections of big data and iot in academic publications a topic modeling approach |
| topic | IoT big data Latent Dirichlet Allocation (LDA) Natural Language Processing (NLP) network data |
| url | https://www.mdpi.com/1424-8220/25/3/906 |
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