Machine learning approaches to identify the data types in big data environment: An overview




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  • V Vijayaraj Research Scholar
  • M. Balamurugan Professor, Department of Computer Science, Bharathidasan University, Tiruchirappalli, Tamil Nadu, India.
  • Monisha Oberai Co-Supervisor, Director, Security Services Sales, IBM, Singapore. APAC


The digital world has access to a multitude of data in the Fourth Industrial Revolution (4IR, also known as Industry 4.0) era, including Internet of Things (IoT) data, cybersecurity data, mobile data, business data, social media data, health data, etc. These data are produced from various sources. Knowledge of artificial intelligence (AI), specifically machine learning (ML), is essential to intelligently analyse these data and create the associated smart and automated applications. Many different kinds of machine learning algorithms exist in the field, including supervised, unsupervised, semi-supervised, and reinforcement learning. Additionally, deep learning, which belongs to a larger family of machine learning techniques, has the ability to effectively examine a lot of data. In this article, we provide a thorough overview of these machine learning techniques that may be used to improve the functionality and intelligence of an application. Determining the fundamentals of various machine learning approaches and how they can be used in identifying the data types and classify them to be placed in bigdata nodes for the effective storage and retrieval, is thus the core contribution of this work. Based on our findings, we also discuss the difficulties and potential possibilities for future research.

How to Cite

Vijayaraj, V., Balamurugan, M., & Oberai, M. (2023). Machine learning approaches to identify the data types in big data environment: An overview. The Scientific Temper, 14(03), 950–956.


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