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The Internet of Things generates massive data. This study introduces Apache Spark as a platform to enhance machine learning for big data analysis, focusing on regression, classification, and clustering.

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Area of Science:

  • Data Science
  • Computer Science
  • Artificial Intelligence

Background:

  • The proliferation of connected devices and digital platforms generates unprecedented volumes of data, termed big data.
  • Big data analysis is crucial for uncovering trends, relationships, and insights to inform decision-making.
  • Traditional machine learning methods struggle with the scale and complexity of big data challenges.

Purpose of the Study:

  • To introduce Apache Spark as a scalable platform for big data processing.
  • To demonstrate how machine learning algorithms can be effectively implemented on Spark.
  • To explore the application of regression, classification, and clustering techniques within the Spark ecosystem.

Main Methods:

  • Introduction to Apache Spark architecture for distributed data processing.
  • Application of machine learning algorithms (regression, classification, clustering) on the Spark platform.
  • Focus on addressing challenges in designing and executing big data systems using Spark.

Main Results:

  • Spark provides a robust framework for handling large-scale datasets.
  • Machine learning models can be efficiently deployed on Spark for big data analysis.
  • Demonstrated applicability of regression, classification, and clustering on Spark.

Conclusions:

  • Apache Spark is a suitable platform for overcoming big data challenges in machine learning.
  • The integration of machine learning with Spark enables advanced data analysis and prediction.
  • Further research can explore more complex machine learning models and applications within Spark.