Deep learning applications and challenges in big data analytics

Deep learning applications and challenges in big data analytics


EOI: 10.11242/viva-tech.01.06.018

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Citation

Prof. Neha Lodhe, Mr. Sumit Bhatkar, Ms. Neha Tiwari, "Deep learning applications and challenges in big data analytics", VIVA-IJRI Volume 1, Issue 6, Article MCA_18, pp. 1-6, 2023. Published by MCA Department, VIVA Institute of Technology, Virar, India.

Abstract

Two areas of data science with a lot of interest are big data analytics and deep learning. Big Data has grown in importance as a result of the large-scale collection of domain-specific data by both public and private entities, which can provide useful information regarding issues like national intelligence, cyber security, fraud detection, marketing, and medical informatics. Large data sets are being analysed by businesses like Google and Microsoft for business analysis and decisions that will affect both current and future technologies. Via a hierarchical learning process, deep learning algorithms extract high-level, complex abstractions as data representations. Based on relatively simpler abstractions created in the previous level of the hierarchy, complex abstractions are learned at a given level. Massive amounts of unsupervised data can be analysed and learned from using deep learning, which makes big data analytics possible even when the raw data is largely unlabeled and uncategorized. In this work, we investigate how Deep Learning might be used to solve certain key issues in Big Data Analytics, such as extracting intricate patterns from enormous amounts of data, semantic indexing, data tagging, quick information retrieval, and simplification of discriminative tasks. We also look into several Deep Learning research areas that require more investigation in order to address specific Big Data Analytics difficulties, such as streaming data, high-dimensional data, model scalability, and distributed computing. Defining data sample criteria, domain adaption modelling, establishing criteria for generating meaningful data abstractions, enhancing semantic indexing, semi-supervised learning, and active learning are some of the problems we pose in our conclusion to provide insights into pertinent future studies.

Keywords

Big data, Data Analytics, Data Mining, Deep learning, Machine Learning

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