A Conceptual Framework for Intelligent Integration of Heterogeneous Big Data Sources

Abstract

The exponential growth of digital technologies has resulted in the generation of massive volumes of data from diverse sources, including enterprise applications, relational databases, cloud platforms, Internet of Things (IoT) devices, web services, and social media networks. These heterogeneous data sources produce structured, semi-structured, and unstructured information that presents significant challenges for organizations attempting to obtain unified and meaningful insights. Conventional data integration approaches primarily depend on static Extract– Transform–Load (ETL) processes and manually defined schema mappings, which are often inadequate for modern big data environments characterized by dynamic data generation, scalability requirements, and semantic diversity. This paper proposes a conceptual Intelligent Data Integration Framework that provides a systematic approach for integrating heterogeneous data into a unified repository. The proposed framework consists of multiple conceptual stages, including heterogeneous data acquisition, data preprocessing, schema matching, entity resolution, semantic integration, metadata management, and centralized storage. The framework is designed to improve data consistency, interoperability, scalability, and overall data quality while reducing the complexity associated with integrating multiple heterogeneous data sources. Unlike implementation-oriented solutions, this work focuses on the conceptual design and logical workflow of the framework without experimental validation. The proposed framework establishes a theoretical foundation for future intelligent data integration systems capable of supporting business intelligence, predictive analytics, scientific research, healthcare, finance, smart city applications, and other data-intensive domains. The conceptual analysis demonstrates that the proposed framework has the potential to address many limitations of traditional integration approaches by providing a flexible, scalable, and intelligent architecture for heterogeneous big data integration.