Muhammed, M.T. , Obidallah, W.J. and Raahemi, B
2018. Applying Deep Learning Techniques for Big Data Analytics: A Systematic Literature Review. Archives of Information Science and Technology, 1(1): 20-41.
Publication year: 2018

Context: Data are being generated from numerous sources and applications and are thereby becoming increasingly complex. The increase in the use of technologies-such as phones, machines, vehicles, sports activities, and academic activities to carry out social and economic functions has also led to various forms of data being generated. The complexity, velocity, versatility, and volume of these data have introduced “big data”, which is also called “large data”. Because big data analysis is becoming a challenge to the exponential growth of data, deep learning, which is an aspect of machine learning, is considered a method of analyzing big data due to its use of excellent and advanced classification techniques and the hierarchical layer techniques. In this paper, we analyze how deep learning techniques and algorithms have been applied to big data, the types of datasets, the algorithms used, and the trend toward this area of study. Objective: To identify, summarize, and systematically compare the current deep learning techniques and algorithms for big data; the various datasets to which deep learning algorithms are mostly applied; and the areas and fields in which the selected studies are being conducted, while providing answers to a specific set of research questions that goes thus: What are the relevant techniques, methods, and algorithms of deep learning in big data analysis? What are the most common datasets used for validation? And What are the trends and future research directions? These research questions helps us focus on the algorithms used in big data, which also helps with the results section of our paper, type of dataset that is being used for deep learning algorithms, and lastly to identify the most common area i.e. the field of study, the country, and the year in which this has trended. Method: We conducted a systematic literature review (SLR) using predefined procedures that involved automatically searching five public digital libraries: IEEE, SCOPUS, ScienceDirect, Web of Science, and the ACM Digital Library. Of the original 863 papers retrieved from these search engines and during our two-stage scanning, 74 primary studies were identified, and we eventually selected 33 final papers, which we used in the synthesis of this study. Results: This SLR includes definitions of big data and deep learning, the areas in which deep learning algorithms have been applied to big data, and the various types of deep learning techniques and algorithms used for big data. A synthesis that resulted in knowledge of the current state of the art in deep learning algorithms applied to big data. This SLR also identifies the trends in deep learning research that have been applied to big data over the past 10 years. Conclusion: The application of deep learning has gained considerable attention since 2015. However, research needs to be done to improve the ways in which deep learning algorithms will be applied to varieties of data. Based on our review and analysis of the final selected studies, the big datasets to which these deep learning algorithms are applied are mostly image datasets. This demonstrates how well these algorithms can perform on image datasets. Furthermore, our review identifies the trends in the research on deep learning algorithms for big data, which will help researchers to understand the current state of the art regarding the use of deep learning algorithm on big data.

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