Artificial Intelligence in Big Data: A Literature Review
Keywords:
Artificial Intelligence, Big Data, Deep Learning, Federated Learning, Large Language Models, Edge AI, Systematic Literature Review.Abstract
This Artificial Intelligence (AI) and Big Data combination has revolutionized data-driven decision-making in almost all the scientific and industrial fields. Over the last six years, from 2020 to 2026, the field witnessed an unprecedented degree of change, as deep learning became more mature, transformer architectures and large language models emerged and became more popular, federated learning and edge learning began to gain farming, and privacy and algorithmic accountability became stricter. This paper is a systematic review of the literature in Scopus and Web of Science following PRISMA 2020 guidelines. After deduping records, two stage screening and quality checking with Kitchenham & Charters criteria, 138 Primary studies were selected from a pool of 4182 records. The objective of this research is to bring together the evidence for the four research questions. Results indicate the deep-learning family continues to be the top applied family (46.4% of studies), but the field is on a structural shift with the transformer- and LLM-based approaches rising from less than 3.0% in 2020, to more than 28% in 2025, and with federated-learning slightly more than 9% jumping to roughly tripled in the same timeframe. Application areas and domains are still largely dominated by the areas of healthcare, IoT/smart cities, and finance, while scalability, privacy, explainability, data quality, and energy efficiency remain among the most frequently cited open problems. The paper ends with a research agenda consisting of six points, most of them focusing on trustworthy, sustainable, and human-centric AI for Big Data.
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