Intersections of Big Data and IoT in Academic Publications: A Topic Modeling Approach

Area of Science:

• Computer Science
• Information Science
• Engineering

Background:

• Big Data analytics and Internet of Things (IoT) are transforming industries by enabling informed decisions and real-time data exchange.
• The convergence of Big Data and IoT drives innovations in areas like predictive maintenance and smart city solutions.
• Understanding research trends in these intersecting fields is crucial for future development.

Purpose of the Study:

• To identify and analyze key themes and trends in Big Data and IoT research.
• To uncover the evolving landscape of Big Data and IoT through bibliometric analysis.
• To provide insights into the dominant research topics within the Big Data and IoT domains.

Main Methods:

• A dataset of 8159 publications was sourced from the Web of Science database.
• Natural Language Processing (NLP) techniques were employed to analyze abstracts, titles, and keywords.
• Latent Dirichlet Allocation (LDA) was used to extract six distinct research topics, supplemented by selective human validation.

Main Results:

• Topic 1: Data systems and IoT technologies, particularly in smart systems and energy applications.
• Topic 2: Application of technologies across various industries.
• Topic 3: Machine learning and IoT applications, focusing on new algorithms and models.
• Topic 4: Smart technologies and systems.
• Topic 5: Digital transformation within industrial supply chains.
• Topic 6: Technical aspects including modeling, system performance, and prediction algorithms for IoT networks.

Conclusions:

• The research successfully identified six core research topics at the intersection of Big Data and IoT.
• The findings highlight the diverse applications and technical advancements within these fields.
• This analysis provides a valuable overview of current Big Data and IoT research, guiding future investigations.