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 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 , likely in the context of smart systems and energy-related applications. Topic 2 focuses on the , 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 . 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 . Topic 5 touches upon the role of digital chains and supply systems, suggesting an industrial focus on . Topic 6 focuses on technical aspects such as . It delves into the efficiency of IoT networks with terms like "accuracy", "power" and "performance" standing out.