Large Dataset Classification Using Parallel Processing Concept

Mohammad Aljanabi - Department of computer science, Alsalam university college, Baghdad, Iraq
Hind Ra'ad Ebraheem - Department of computer science, Alsalam university college, Baghdad, Iraq
Zahraa Faiz Hussain - Department of computer science, Alsalam university college, Baghdad, Iraq
Mohd Farhan Md Fudzee - Faculty of Computer Science and Information Technology, University Tun Hussein Onn Malaysia (UTHM), Malaysia
Shahreen Kasim - Faculty of Computer Science and Information Technology, University Tun Hussein Onn Malaysia (UTHM), Malaysia
Mohd Arfian Ismail - Faculty of Computing, College of Computing and Applied Sciences, University Malaysia Pahang, Pahang, Malaysia
Dwiny Meidelfi - Department of Information Technology, Politeknik Negeri Padang, Sumatera Barat, Indonesia
Aldo Erianda - Department of Information Technology, Politeknik Negeri Padang, Sumatera Barat, Indonesia


Citation Format:



DOI: http://dx.doi.org/10.30630/joiv.4.4.361

Abstract


Much attention has been paid to large data technologies in the past few years mainly due to its capability to impact business analytics and data mining practices, as well as the possibility of influencing an ambit of a highly effective decision-making tools. With the current increase in the number of modern applications (including social media and other web-based and healthcare applications) which generates high data in different forms and volume, the processing of such huge data volume is becoming a challenge with the conventional data processing tools. This has resulted in the emergence of big data analytics which also comes with many challenges. This paper introduced the use of principal components analysis (PCA) for data size reduction, followed by SVM parallelization. The proposed scheme in this study was executed on the Spark platform and the experimental findings revealed the capability of the proposed scheme to reduce the classifiers’ classification time without much influence on the classification accuracy of the classifier.

Keywords


Large dataset; Parallel SVMs; PCA; Apache Spark.

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JOIV : International Journal on Informatics Visualization
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