Navigating Data Overload: A Bibliometric Analysis of Trends and Challenges in Real-Time Data Processing (2020–2026)
Keywords:
Real-Time Data; Data Overload; Streaming Analytics; Scopus-Indexed Publications; Bibliometric AnalysisAbstract
The continuous growth of digital technologies has resulted in the generation of massive volumes of data that must be processed and acted upon in real time. Applications such as smart cities, healthcare monitoring, financial systems, industrial automation, and intelligent transportation increasingly rely on real-time data streams to support timely decision-making. However, this dependence on real-time data has intensified the problem of data overload, where the volume, velocity, and complexity of incoming data exceed the processing capabilities of systems or the cognitive capacity of human users. This paper provides a comprehensive discussion of real-time data processing in the context of data overload, focusing on technical and human-centered challenges and emerging solutions such as edge computing, artificial intelligence, and streaming analytics. A total of 193 documents published between 2020 and 2026 were retrieved from the Scopus database and were identified based on predefined search criteria. The documents are then analyzed using VOSviewer using keyword co-occurrence and the results generated shows that the publication trend analysis reveals a clear upward trajectory in research output from 2020 to 2026, with a particularly sharp increase after 2021. Based on the visualization results, in summary, there are four clusters generated which reveal a multi-layered research structure where Cluster 1 focuses on intelligent algorithms and cognitive capabilities, Cluster 2 provides the distributed infrastructure and system efficiency, Cluster 3 demonstrates domain-specific, real-time experimental applications, and Cluster 4 emphasizes human interaction, data integration, and information use. This growth indicates heightened scholarly interest and reflects broader technological and societal shifts toward intelligent and real-time systems. By providing a structured overview of the field’s intellectual landscape, this study supports informed decision-making and lays the foundation for future research in artificial intelligence-driven real-time systems.
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