Processing big data with decision trees: A case study in large traffic data

Hanif Arief Wisesa, M. Anwar Ma'Sum, Petrus Mursanto, Andreas Febrian

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

This paper provides a comparison of processing large traffic data by using decision trees. The experiment was tested in three different classifier tools that are very popular and are widely used in the community. These classifier tools are WEKA classifier, MoA (Massive Online Analysis) classifier, and SPARK MLib that runs on Hadoop infrastructure. We tested the traffic data using decision trees because it is one of the best methods for regressing the large data. The experiment results showed that the WEKA classifier fails to classify dataset with a large number of instance, wheras the MoA has successfully regress the dataset as a datastream. The SPARK MLib decision trees algorithm could also successfully resgress the traffic data quickly with a fairly good accuracy.

Original languageEnglish
Title of host publication2016 International Workshop on Big Data and Information Security, IWBIS 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages115-120
Number of pages6
ISBN (Electronic)9781509034772
DOIs
Publication statusPublished - 6 Mar 2017
Event2016 International Workshop on Big Data and Information Security, IWBIS 2016 - Jakarta, Indonesia
Duration: 18 Oct 201619 Oct 2016

Publication series

Name2016 International Workshop on Big Data and Information Security, IWBIS 2016

Conference

Conference2016 International Workshop on Big Data and Information Security, IWBIS 2016
CountryIndonesia
CityJakarta
Period18/10/1619/10/16

Keywords

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