Deep Learning Models for Heterogeneous Big Data Analytics

Abstract

in recent times, Big data is modifying the style life of workplaces and thinking by improved performance in knowledge discovering and decision making ever-greater volumes of data are being produced data due to the network of sensors and communication technologies Heterogeneous data is a category of unstructured data with an unknown pace in several ways. Current data analysis techniques are inadequate to handle the huge volumes of data produced, this data difficult to manage, store, handle, interpret, analyze using traditional techniques. Deep learning (DL) is extremely popular among many data scientists and experts thanks to the high precision in speech recognition, image handling, and data analytics. DL has become much more important because it can be used for largescale heterogeneous data. DL has been applied efficiently in several fields and has exceeded most of the traditional techniques, DL algorithmic can study large unclassified data with the ability to select features. This study concentrates on the discussion of a variety of new algorithms that handle this data and DL models that provide greater accuracy for heterogeneous data.