Big Data and Machine Learning Integration: Gap analysis and research trends

Correa P.A.R.; Pineda V.G.; Moreno-López G.; Giraldo L.F.G.; León A.J.O.; Giraldo M.C.B.; Benjumea-Arias M. · RISTI - Revista Iberica de Sistemas e Tecnologias de Informacao (2024)

Resumen

The integration of Big Data and Machine Learning is fundamental to data science and artificial intelligence, enabling in-depth analysis of vast datasets. This study aims to examine trends in this field. Using the PRISMA-2020 methodology, we conducted a wide-ranging bibliometric analysis to identify, gather, and evaluate pertinent literature. Our findings showcase a growing interest in this fusion in diverse industries, particularly in the past few years (2021-2023). Prominent authors Wang L and Wang J have examined thematic evolution in their study, highlighting an adaptation to current trends, including artificial intelligence and social media. Through their examination of keywords, they have revealed emerging approaches and challenges that are relevant to the continued integration of Big Data and Machine Learning in current research. This study emphasizes the ongoing relevance and vitality of these areas in academic research.

Instituciones: Universidad Ricardo Palma