Integrating DM, BDA, and DBS: A Hybrid PRISMA–Bibliometric–LDA Review

This study develops a unified conceptual framework that explains the interdependencies among Data Mining (DM), Big Data Analytics (BDA), and Database Systems (DBS) within modern data architectures. To achieve this objective, the research introduces an extended SLR methodology that triangulates the PRISMA protocol, bibliometric analysis, and topic modeling, enabling the identification of technological clusters, thematic structures, and the evolutionary trajectories of the field. The findings show that the Big Data ecosystem is organized around a conceptual axis of “infrastructure–application–knowledge,” in which distributed infrastructures and NoSQL technologies constitute the architectural foundation, applied analytics links technological capabilities to organizational use cases, and knowledge extraction from unstructured data marks the semantic and cognitive maturation of the domain. Co-citation, co-occurrence, MCA, and thematic-map analyses confirm the existence of distinct yet complementary epistemic schools, while topic modeling highlights the convergence of Big Data architectures, advanced analytics, and operational governance. The study thus contributes to consolidating an integrated perspective on DM–BDA–DBS and provides a replicable methodological framework for exploring conceptual evolution in the Big Data domain.