Deep Learning in Big Data: Challenges and Perspectives

Large amounts of data are frequently available in businesses, and in recent years, there has been an enormous possibility formed about analyzing these data sets. The academic community has scrutinized this expectation and found it helpful in various industries. Nowadays, the terms big data and deep learning are commonly used in the scientific community. Several conventional data processing approaches restrict the processing of vast amounts of data. To achieve precise and efficient real-time data processing, advanced algorithms that incorporate machine and deep learning techniques are required for big data analytics. Despite the difficulties posed by the high volume, variety, velocity, and veracity of big data, recent research has successfully combined various deep learning algorithms with hybrid learning and training processes to enable rapid data analysis. As a result, big data offers significant opportunities for a wide range of industries, including e-commerce, industrial control, and innovative medicine. Despite the potential benefits, mining and processing large quantities of information continue to present challenges that must be addressed. This article examines recent studies on deep learning models for discovering features in massive data. In addition, we discuss upcoming issues and highlight the significant data deep learning barriers that still need to be overcome.