Data abstraction is the reduction of a particular body of data to a simplified representation of the whole.
Published in Chapter:
Investigation on Deep Learning Approach for Big Data: Applications and Challenges
Dharmendra Singh Rajput (VIT University, India), T. Sunil Kumar Reddy (Sri Venkateswara College of Engineering and Technology, India), and Dasari Naga Raju (Sri Venkateswara College of Engineering and Technology, India)
Copyright: © 2018
|Pages: 14
DOI: 10.4018/978-1-5225-3870-7.ch002
Abstract
In recent years, big data analytics is the major research area where the researchers are focused. Complex structures are trained at each level to simplify the data abstractions. Deep learning algorithms are one of the promising researches for automation of complex data extraction from large data sets. Deep learning mechanisms produce better results in machine learning, such as computer vision, improved classification modelling, probabilistic models of data samples, and invariant data sets. The challenges handled by the big data are fast information retrieval, semantic indexing, extracting complex patterns, and data tagging. Some investigations are concentrated on integration of deep learning approaches with big data analytics which pose some severe challenges like scalability, high dimensionality, data streaming, and distributed computing. Finally, the chapter concludes by posing some questions to develop the future work in semantic indexing, active learning, semi-supervised learning, domain adaptation modelling, data sampling, and data abstractions.