Work2019

Big Data Analytics using Machine Learning Techniques

29 citations0/0 files storedInternational Conference on Cloud Computing, Data Science & Engineering (Confluence)

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Big Data Technologies and Applications Cloud Computing and Resource Management Stochastic Gradient Optimization Techniques

Abstract

Gigabytes of data is being generated now a days on daily basis which may possess some of the characteristics such as high speed, huge volume, uncertainty, non-stationary data, real time data etc. Conventional Machine Learning Techniques cannot be used for analysis of big data due to its above mentioned features. Also, traditional storage and processing techniques fails to meet the requirements. In this paper, we have discussed the various challenges that may occur while using traditional MLT for Big Data Analytics and its possible solutions. As per our survey, Parallel Processing, Dimensionality Reduction techniques, GPUs, Map reduce jobs, Deep learning, Online learning, Incremental learning are some of the possible solution to meet the challenges associated with big data analytics.

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References

2015Deep learning applications and challenges in big data analytics 2014Big Data Deep Learning: Challenges and Perspectives 2017A survey on deep learning for big data 2017Machine Learning With Big Data: Challenges and Approaches 2016A survey of machine learning for big data processing 2015Big data analytics: a survey 2017A survey of machine learning algorithms for big data analytics 2016Machine Learning in Big Data

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2021An Intelligent Approach for Data Analysis and Decision Making in Big Data: A Case Study on E-commerce Industry

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