A Multi-dimensional Comparison of Toolkits for Machine Learning with Big Data

Aaron N. Richter; Taghi M. Khoshgoftaar; Sara Landset; Tawfiq Hasanin; IEEE

doi:10.1109/IRI.2015.12

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A Multi-dimensional Comparison of Toolkits for Machine Learning with Big Data

Aaron N. Richter, Taghi M. Khoshgoftaar, Sara Landset, Tawfiq Hasanin and IEEE

2015 IEEE International Conference on Information Reuse and Integration, pp.1-8

01/08/2015

DOI: https://doi.org/10.1109/IRI.2015.12

Abstract

Big data

Clustering algorithms

data mining

Data models

Engines

Hadoop

machine learning

Machine learning algorithms

Mahout

Spark

Sparks

Water

Big data is a big business, and effective modeling of this data is key. This paper provides a comprehensive multidimensional analysis of various open source tools for machine learning with big data. An evaluation standard is proposed along with detailed comparisons of the frameworks discussed, with regard to algorithm availability, scalability, speed, and more. The major tools profiled are Mahout, MLlib, H2O, and SAMOA, along with the big data processing engines they utilize, including Hadoop MapReduce, Apache Spark, and Apache Storm. There is not yet one framework that "does it all", but this paper provides insight into each tool's strengths and weaknesses along with guidance on tool choice for specific needs.

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Details

Title: A Multi-dimensional Comparison of Toolkits for Machine Learning with Big Data
Creators - without role: Aaron N. Richter - Florida Atlantic University
Taghi M. Khoshgoftaar - Florida Atlantic University
Sara Landset - Florida Atlantic University
Tawfiq Hasanin - Florida Atlantic University
IEEE
Publication Details: 2015 IEEE International Conference on Information Reuse and Integration, pp.1-8
Publisher: IEEE
Identifiers: 9934245908331
Academic Unit: King Abdulaziz University
Language: English
Resource Type: Conference proceeding