A Distributed Big Data Analytics Framework for Scalable Knowledge Discovery in Heterogeneous Systems

Abstract

Big data analytics has emerged as a transformative paradigm for extracting valuable knowledge and actionable insights from massive volumes of structured, semi-structured, and unstructured data generated by modern digital systems. The rapid growth of cloud computing, Internet of Things (IoT), social media platforms, sensor networks, healthcare systems, financial transactions, and industrial automation has significantly increased the scale, complexity, and heterogeneity of data environments. Traditional data processing and analytics techniques often struggle to manage large-scale distributed datasets due to limitations in scalability, storage efficiency, computational performance, and real-time processing capability. Consequently, distributed big data analytics frameworks have become essential for scalable knowledge discovery and intelligent decision-making in heterogeneous computing systems. This research proposes a Distributed Big Data Analytics Framework for Scalable Knowledge Discovery in Heterogeneous Systems. The proposed framework integrates distributed computing architectures, parallel data processing, machine learning-driven analytics, and scalable storage mechanisms to enable efficient analysis of large-scale heterogeneous datasets. The framework combines distributed file systems, MapReduce-based parallel processing, stream analytics, and intelligent feature extraction techniques to improve scalability, fault tolerance, and computational efficiency across distributed environments. The proposed architecture supports data ingestion from multiple heterogeneous sources including IoT devices, cloud platforms, enterprise databases, sensor networks, and social media streams. Distributed machine learning and data mining algorithms are employed to perform scalable knowledge discovery, pattern recognition, anomaly detection, and predictive analytics. Experimental evaluation demonstrates that the proposed framework significantly improves processing throughput, scalability, fault tolerance, and analytical accuracy compared to traditional centralized analytics systems. The framework also supports real-time and batch-mode processing for large-scale intelligent applications.