Deep learning applications and challenges in big data analytics

Maryam M Najafabadi,Taghi M Khoshgoftaar,Randall Wald,Edin Muharemagic,Naeem Seliya,Flavio Villanustre

doi:10.1186/s40537-014-0007-7

Maryam M Najafabadi, Taghi M Khoshgoftaar + Show 4 more

Open Access

https://doi.org/10.1186/s40537-014-0007-7

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Abstract

AbstractBig Data Analytics and Deep Learning are two high-focus of data science. Big Data has become important as many organizations both public and private have been collecting massive amounts of domain-specific information, which can contain useful information about problems such as national intelligence, cyber security, fraud detection, marketing, and medical informatics. Companies such as Google and Microsoft are analyzing large volumes of data for business analysis and decisions, impacting existing and future technology. Deep Learning algorithms extract high-level, complex abstractions as data representations through a hierarchical learning process. Complex abstractions are learnt at a given level based on relatively simpler abstractions formulated in the preceding level in the hierarchy. A key benefit of Deep Learning is the analysis and learning of massive amounts of unsupervised data, making it a valuable tool for Big Data Analytics where raw data is largely unlabeled and un-categorized. In the present study, we explore how Deep Learning can be utilized for addressing some important problems in Big Data Analytics, including extracting complex patterns from massive volumes of data, semantic indexing, data tagging, fast information retrieval, and simplifying discriminative tasks. We also investigate some aspects of Deep Learning research that need further exploration to incorporate specific challenges introduced by Big Data Analytics, including streaming data, high-dimensional data, scalability of models, and distributed computing. We conclude by presenting insights into relevant future works by posing some questions, including defining data sampling criteria, domain adaptation modeling, defining criteria for obtaining useful data abstractions, improving semantic indexing, semi-supervised learning, and active learning.

Highlights

The general focus of machine learning is the representation of the input data and generalization of the learnt patterns for use on future unseen data
With respect to the first topic, we explore the application of Deep Learning for specific Big Data Analytics, including learning from massive volumes of data, semantic indexing, discriminative tasks, and data tagging
In contrast to more conventional machine learning and feature engineering algorithms, Deep Learning has an advantage of potentially providing a solution to address the data analysis and learning problems found in massive volumes of input data

Summary

Introduction

The general focus of machine learning is the representation of the input data and generalization of the learnt patterns for use on future unseen data. We conclude by identifying important future areas needing innovation in Deep Learning for Big Data Analytics, including data sampling for generating useful high-level abstractions, domain (data distribution) adaption, defining criteria for extracting good data representations for discriminative and indexing tasks, semi-supervised learning, and active learning.

Results

Conclusion