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Related Experiment Video

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Constructing and Visualizing Models using Mime-based Machine-learning Framework

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Published on: July 22, 2025

Paradigms for Realizing Machine Learning Algorithms.

Vijay Srinivas Agneeswaran¹, Pranay Tonpay¹, Jayati Tiwary¹

¹Impetus Infotech India Private Limited Bangalore, Karnataka, India.

|July 23, 2016

Summary

This summary is machine-generated.

Explore three generations of machine learning algorithms designed for big data. Discover how newer paradigms beyond Hadoop

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Area of Science:

Computer Science
Artificial Intelligence
Data Science

Background:

Machine learning algorithms are increasingly applied to big data challenges.
Traditional approaches often face limitations with large-scale datasets.

Purpose of the Study:

To outline the evolution of machine learning tools across three generations.
To highlight the need to move beyond the Hadoop Map-Reduce paradigm for big data analytics.
To introduce emerging third-generation technologies for advanced big data analysis.

Main Methods:

Review of machine learning algorithms and their generational development.
Analysis of big data processing paradigms, including Hadoop Map-Reduce.
Identification of third-generation tools like Spark and GraphLab.

Main Results:

First-generation tools (e.g., SAS, SPSS) have limitations with big data.
Second-generation tools (e.g., Mahout, RapidMiner) leverage Hadoop but have constraints.
Third-generation paradigms (e.g., Spark, GraphLab) offer advanced capabilities for big data.

Conclusions:

Effective big data machine learning requires moving beyond Hadoop Map-Reduce.
Third-generation tools provide powerful frameworks for deep analytics on big data.
Emerging technologies are crucial for unlocking the full potential of big data machine learning.