Efficiently Processing Big Data in Real-Time Employing Deep Learning Algorithms

2018 ◽  
pp. 61-78 ◽  
Author(s):  
Murad Khan ◽  
Bhagya Nathali Silva ◽  
Kijun Han
Keyword(s):  
Neural Networks ◽  
Big Data ◽  
Deep Learning ◽  
Data Analytics ◽  
Learning Algorithms ◽  
Big Data Analytics ◽  
High Time ◽  
Digital Data ◽  
Learning Mechanisms ◽  
Digital World

Big Data and deep computation are among the buzzwords in the present sophisticated digital world. Big Data has emerged with the expeditious growth of digital data. This chapter addresses the problem of employing deep learning algorithms in Big Data analytics. Unlike the traditional algorithms, this chapter comes up with various solutions to employ advanced deep learning mechanisms with less complexity and finally present a generic solution. The deep learning algorithms require less time to process the big amount of data based on different contexts. However, collecting the accurate feature and classifying the context into patterns using neural networks algorithms require high time and complexity. Therefore, using deep learning algorithms in integration with neural networks can bring optimize solutions. Consequently, the aim of this chapter is to provide an overview of how the advance deep learning algorithms can be used to solve various existing challenges in Big Data analytics.

Efficiently Processing Big Data in Real-Time Employing Deep Learning Algorithms

2020 ◽  
pp. 1344-1357
Author(s):  
Murad Khan ◽  
Bhagya Nathali Silva ◽  
Kijun Han
Keyword(s):  
Neural Networks ◽  
Big Data ◽  
Deep Learning ◽  
Data Analytics ◽  
Learning Algorithms ◽  
Big Data Analytics ◽  
High Time ◽  
Digital Data ◽  
Learning Mechanisms ◽  
Digital World

Big Data and deep computation are among the buzzwords in the present sophisticated digital world. Big Data has emerged with the expeditious growth of digital data. This chapter addresses the problem of employing deep learning algorithms in Big Data analytics. Unlike the traditional algorithms, this chapter comes up with various solutions to employ advanced deep learning mechanisms with less complexity and finally present a generic solution. The deep learning algorithms require less time to process the big amount of data based on different contexts. However, collecting the accurate feature and classifying the context into patterns using neural networks algorithms require high time and complexity. Therefore, using deep learning algorithms in integration with neural networks can bring optimize solutions. Consequently, the aim of this chapter is to provide an overview of how the advance deep learning algorithms can be used to solve various existing challenges in Big Data analytics.

scholarly journals Deep Learning for Big Data Analytics

2019 ◽  
pp. 1-21
Author(s):  
Priti Srinivas Sajja ◽  
Rajendra Akerkar
Keyword(s):  
Neural Networks ◽  
Big Data ◽  
Deep Learning ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Future Research ◽  
Cognitive Computing ◽  
Intelligent Support ◽  
Artificial Neural ◽  
Traditional Approaches

Traditional approaches like artificial neural networks, in spite of their intelligent support such as learning from large amount of data, are not useful for big data analytics for many reasons. The chapter discusses the difficulties while analyzing big data and introduces deep learning as a solution. This chapter discusses various deep learning techniques and models for big data analytics. The chapter presents necessary fundamentals of an artificial neural network, deep learning, and big data analytics. Different deep models such as autoencoders, deep belief nets, convolutional neural networks, recurrent neural networks, reinforcement learning neural networks, multi model approach, parallelization, and cognitive computing are discussed here, with the latest research and applications. The chapter concludes with discussion on future research and application areas.

Investigation on Deep Learning Approach for Big Data

2018 ◽  
pp. 25-38
Author(s):  
Dharmendra Singh Rajput ◽  
T. Sunil Kumar Reddy ◽  
Dasari Naga Raju
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Data Analytics ◽  
Probabilistic Models ◽  
Big Data Analytics ◽  
Data Sets ◽  
Semantic Indexing ◽  
Learning Approaches ◽  
Data Sampling ◽  
Learning Mechanisms

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.

Investigation on Deep Learning Approach for Big Data

2020 ◽  
pp. 1016-1029
Author(s):  
Dharmendra Singh Rajput ◽  
T. Sunil Kumar Reddy ◽  
Dasari Naga Raju
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Data Analytics ◽  
Probabilistic Models ◽  
Big Data Analytics ◽  
Data Sets ◽  
Semantic Indexing ◽  
Learning Approaches ◽  
Data Sampling ◽  
Learning Mechanisms

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.

Sign in / Sign up

Export Citation Format

Share Document