Potential deep learning solutions to persistent and emerging big data challenges-a practitioners- cookbook

Author Affiliation

Tahir Q. Syed is Assistant Professor at Institute of Business Administration (IBA), Karachi

Faculty / School

School of Mathematics and Computer Science (SMCS)

Department

Department of Computer Science

Was this content written or created while at IBA?

Yes

Document Type

Article

Source Publication

ACM Computing Surveys

ISSN

0360-0300

Disciplines

Computer Sciences | Mathematics

Abstract

The phenomenon of Big Data continues to present moving targets for the scientific and technological state-of-The-Art. This work demonstrates that the solution space of these challenges has expanded with deep learning now moving beyond traditional applications in computer vision and natural language processing to diverse and core machine learning tasks such as learning with streaming and non-iid-data, partial supervision, and large volumes of distributed data while preserving privacy. We present a framework coalescing multiple deep methods and corresponding models as responses to specific Big Data challenges. First, we perform a detailed per-challenge review of existing techniques, with benchmarks and usage advice, and subsequently synthesize them together into one organic construct that we discover principally uses extensions of one underlying model, the autoencoder. This work therefore provides a synthesis where challenges at scale across the Vs of Big Data could be addressed by new algorithms and architectures being proposed in the deep learning community. The value being proposed to the reader from either community in terms of nomenclature, concepts, and techniques of the other would advance the cause of multi-disciplinary, transversal research and accelerate the advance of technology in both domains.

Indexing Information

HJRS - W Category, Scopus, Web of Science - Science Citation Index Expanded (SCI)

Publication Status

Published

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