Data Streaming in Concept-Dependent Granulation

In the evolving framework of granular computing, the continuous flow of data, often referred to as data streaming, presents both challenges and opportunities for the granulation process. Building upon the foundational works of Professor Zadeh and the granulation techniques based in rough set theory, this paper investigates the integration of data streaming with the concept-dependent granulation method. Recognizing the inherent challenges posed by the vastness and dynamism of streaming data, we explore the potential of adapting the concept-dependent granulation to accommodate and process these streams efficiently. Drawing inspiration from our previous works on random sampling and data decomposition, we introduce a novel approach that utilizes the real-time nature of data streams to enhance the granulation process. Our experimental studies, aim to evaluate the effectiveness of this method in terms of granulation quality and computational efficiency. Preliminary results suggest that integrating data streaming with concept-dependent granulation not only preserves the integrity of the granulated information but also offers significant advantages in processing large-scale dynamic data. In this work, we have verified the possibility of detecting the amount of data necessary to achieve the relevant classification efficiency, without having to process the entire data. This paper serves as a bridge, connecting the established methodologies of granulation with the emerging challenges of big data streaming, and sets the foundation for future research in this domain.