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Performance Evaluation of Data-driven Intelligent Algorithms for Big data Ecosystem.

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This study evaluates Apache Spark MLlib

Keywords:
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Area of Science:

  • Big Data Analytics
  • Machine Learning
  • Artificial Intelligence

Background:

  • Machine learning algorithms are computationally intensive, requiring significant resources for processing large datasets.
  • The daily generation of quintillion bytes of data necessitates robust big data infrastructure.
  • Apache Spark MLlib is a prominent open-source platform for big data analysis and machine learning applications.

Purpose of the Study:

  • To evaluate and compare the performance of Apache Spark MLlib against other platforms like Rapid Miner and Sklearn.
  • To assess the capabilities of various machine learning algorithms within the Spark MLlib framework.
  • To determine the robustness of PySpark for classification, regression, and unsupervised learning tasks.

Main Methods:

  • Compared Apache Spark MLlib with Rapid Miner and Sklearn for big data processing.
  • Evaluated classification algorithms: Logistic Classifier, Decision Tree Classifier, Random Forest Classifier, and Gradient Boosted Tree Classifier.
  • Tested regression algorithms: Linear Regressor, Decision Tree Regressor, Random Forest Regressor, and Gradient Boosted Tree Regressor.
  • Assessed unsupervised learning methods: K-means and Gaussian Mixture Models.
  • Utilized datasets: SUSY, HIGGS, BANK, and HEPMASS from the UCI repository.

Main Results:

  • Apache Spark MLlib demonstrated scalability and efficiency in handling large datasets for machine learning tasks.
  • Comparative analysis provided insights into the performance characteristics of different ML algorithms across platforms.
  • PySpark proved robust for various machine learning model types, including classification, regression, and clustering.

Conclusions:

  • Apache Spark MLlib is a viable, scalable, and efficient platform for big data machine learning.
  • The study offers a benchmark for selecting appropriate ML algorithms and platforms for big data challenges.
  • Future research should explore advanced machine learning techniques and optimizations within big data environments.