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2017•Automatic control and information sciencesOpen access

Heterogeneous Data and Big Data Analytics

Lidong Wang

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Abstract

Heterogeneity is one of major features of big data and heterogeneous data result in problems in data integration and Big Data analytics. This paper introduces data processing methods for heterogeneous data and Big Data analytics, Big Data tools, some traditional data mining (DM) and machine learning (ML) methods. Deep learning and its potential in Big Data analytics are analysed. The benefits of the confluences among Big Data analytics, deep learning, high performance computing (HPC), and heterogeneous computing are presented. Challenges of dealing with heterogeneous data and Big Data analytics are also discussed.

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What this paper is about

Heterogeneity is one of major features of big data and heterogeneous data result in problems in data integration and Big Data analytics. This paper introduces data processing methods for heterogeneous data and Big Data analytics, Big Data tools, some traditional data mining (DM) and machine learning (ML) methods. Deep learning and its potential in Big Data analytics are analysed. The benefits of the confluences among Big Data analytics, deep learning, high performance computing (HPC), and heterogeneous computing are presented. Challenges of dealing with heterogeneous data and Big Data analytics are also discussed.

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Available abstract

Heterogeneity is one of major features of big data and heterogeneous data result in problems in data integration and Big Data analytics. This paper introduces data processing methods for heterogeneous data and Big Data analytics, Big Data tools, some traditional data mining (DM) and machine learning (ML) methods. Deep learning and its potential in Big Data analytics are analysed. The benefits of the confluences among Big Data analytics, deep learning, high performance computing (HPC), and heterogeneous computing are presented. Challenges of dealing with heterogeneous data and Big Data analytics are also discussed.

Key concepts: Big data, Computer science, Data science, Data analysis, Analytics, Data mining

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