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Essentiality of Machine Learning Algorithms for Big Data Computation
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Author(s): Manjunath Thimmasandra Narayanapppa (BMS Institute of Technology, India), T. P. Puneeth Kumar (Acharya Institute of Technology, India)and Ravindra S. Hegadi (Solapur University, India)
Copyright: 2016
Pages: 12
Source title:
Managing and Processing Big Data in Cloud Computing
Source Author(s)/Editor(s): Rajkumar Kannan (King Faisal University, Saudi Arabia), Raihan Ur Rasool (King Faisal University, Saudi Arabia), Hai Jin (Huazhong University of Science and Technology, China)and S.R. Balasundaram (National Institute of Technology, Tiruchirappalli, India)
DOI: 10.4018/978-1-4666-9767-6.ch011
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Abstract
Recent technological advancements have led to generation of huge volume of data from distinctive domains (scientific sensors, health care, user-generated data, finical companies and internet and supply chain systems) over the past decade. To capture the meaning of this emerging trend the term big data was coined. In addition to its huge volume, big data also exhibits several unique characteristics as compared with traditional data. For instance, big data is generally unstructured and require more real-time analysis. This development calls for new system platforms for data acquisition, storage, transmission and large-scale data processing mechanisms. In recent years analytics industries interest expanding towards the big data analytics to uncover potentials concealed in big data, such as hidden patterns or unknown correlations. The main goal of this chapter is to explore the importance of machine learning algorithms and computational environment including hardware and software that is required to perform analytics on big data.
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