OPTIMIZING BIG DATA PROCESSING THROUGH ARTIFICIAL INTELLIGENCE: A SYSTEMATIC LITERATURE REVIEW
DOI:
https://doi.org/10.1234/aira.v1i2.36Keywords:
Big Data, Artificial Intelligence, Data Processing, Machine Learning, AnalyticsAbstract
The exponential growth of big data presents significant challenges for traditional processing methods, driving the need for more efficient solutions. This systematic literature review explores the integration of artificial intelligence (AI) in big data processing, examining its transformative role in enhancing speed, scalability, and insight extraction. The review, conducted by following systematic guidelines, synthesizes findings from key academic databases such as IEEE Xplore, ACM Digital Library, and Scopus. We identify and discuss the primary advantages of this convergence, including the automation of data lifecycle, enhanced predictive analytics, and improved decision-making. The paper also highlights critical challenges such as data privacy, algorithmic bias, and resource requirements. By leveraging AI's capabilities, organizations can unlock unprecedented opportunities across various domains, from healthcare and finance to retail and smart cities. The review concludes that while the synergy between AI and big data offers immense potential, its full realization depends on addressing these challenges through ethical frameworks, transparent models, and strategic investments in governance and upskilling.
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