From Data Streams to Knowledge Streams: Event-Driven AI Frameworks for Dynamic Scientometric Analysis

Abstract

The exponential growth of scientific data has created unprecedented challenges in knowledge analysis, dissemination, and evaluation. Traditional scientometric approaches, relying primarily on static datasets and retrospective analyses, often fail to capture the dynamic evolution of interdisciplinary research. This paper introduces a novel framework titled 'From Data Streams to Knowledge Streams', which integrates Event-Driven Architecture (EDA) and Artificial Intelligence (AI) to enable real-time scientometric analysis. The proposed architecture processes continuous data streams—such as citation updates, publication feeds, and collaboration metrics—using event-driven mechanisms combined with machine learning and natural language processing. Through asynchronous communication, data integration pipelines, and Event Knowledge Graphs (EKGs), the framework facilitates adaptive and contextualized mapping of scientific knowledge flows. Case studies across healthcare, geohazard research, and education demonstrate how this model improves trend prediction, interdisciplinary collaboration analysis, and policy evaluation. The study also explores ethical and governance implications related to bias, transparency, and data representation in AI-driven informetrics. By merging real-time analytics with cognitive automation, the research establishes a pathway toward autonomous, explainable, and continuously adaptive scientometric ecosystems that can respond dynamically to the evolving global research landscape.