Exploring the Intersection of Machine Learning and Big Data: A Survey - Elias Dritsas; Maria Trigka (Machine Learning and Knowledge Extraction)
The integration of machine learning (ML) with big data has revolutionized industries by enabling the extraction of valuable insights from vast and complex datasets. This convergence has fueled advancements in various fields, leading to the development of sophisticated models capable of addressing complicated problems. However, the application of ML in big data environments presents significant challenges, including issues related to scalability, data quality, model interpretability, privacy, and the handling of diverse and high-velocity data. This survey provides a comprehensive overview of the current state of ML applications in big data, systematically identifying the key challenges and recent advancements in the field. By critically analyzing existing methodologies, this paper highlights the gaps in current research and proposes future directions for the development of scalable, interpretable, and privacy-preserving ML techniques. Additionally, this survey addresses the ethical and societal implications of ML in big data, emphasizing the need for responsible and equitable approaches to harnessing these technologies. The insights presented in this paper aim to guide future research and contribute to the ongoing discourse on the responsible integration of ML and big data.