Investigation on Deep Learning Approach for Big Data: Applications and Challenges

Investigation on Deep Learning Approach for Big Data: Applications and Challenges

Dharmendra Singh Rajput, T. Sunil Kumar Reddy, Dasari Naga Raju
ISBN13: 9781522538707|ISBN10: 1522538704|EISBN13: 9781522538714
DOI: 10.4018/978-1-5225-3870-7.ch002
Cite Chapter Cite Chapter

MLA

Rajput, Dharmendra Singh, et al. "Investigation on Deep Learning Approach for Big Data: Applications and Challenges." Handbook of Research on Pattern Engineering System Development for Big Data Analytics, edited by Vivek Tiwari, et al., IGI Global, 2018, pp. 25-38. https://doi.org/10.4018/978-1-5225-3870-7.ch002

APA

Rajput, D. S., Reddy, T. S., & Raju, D. N. (2018). Investigation on Deep Learning Approach for Big Data: Applications and Challenges. In V. Tiwari, R. Thakur, B. Tiwari, & S. Gupta (Eds.), Handbook of Research on Pattern Engineering System Development for Big Data Analytics (pp. 25-38). IGI Global. https://doi.org/10.4018/978-1-5225-3870-7.ch002

Chicago

Rajput, Dharmendra Singh, T. Sunil Kumar Reddy, and Dasari Naga Raju. "Investigation on Deep Learning Approach for Big Data: Applications and Challenges." In Handbook of Research on Pattern Engineering System Development for Big Data Analytics, edited by Vivek Tiwari, et al., 25-38. Hershey, PA: IGI Global, 2018. https://doi.org/10.4018/978-1-5225-3870-7.ch002

Export Reference

Mendeley
Favorite

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.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.