Abstract
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”.
Highlights
As big data is gaining recognition and is a fast-moving target area of focus in our current technology state and societies, it is exceeding the amount of data we used to have
This systematic literature review (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
Summary
As big data is gaining recognition and is a fast-moving target area of focus in our current technology state and societies, it is exceeding the amount of data we used to have. Big data generally refers to a volume of data that cannot be processed effectively using ordinary database methods [1,2] proposed a useful definition based on the literature and journals they consulted for their research, observation, and analysis of the essence of big data. According to their definition, “big data is a set of techniques and technologies that require new forms of integration to uncover large hidden values from large datasets that are diverse, complex, and of a massive scale” [2]. The following attributes can be used to define big data: volume, velocity, veracity and variety [5]
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