2-3 September 2021
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Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.
ParaDist-HMM: A Parallel Distributed Implementation of Hidden Markov Model for Big Data Analytics using Spark
Digital Object Identifier (DOI) : 10.14569/IJACSA.2021.0120438
Article Published in International Journal of Advanced Computer Science and Applications(IJACSA), Volume 12 Issue 4, 2021.
Abstract: Big Data is an extremely massive amount of hetero-geneous and multisource data which often requires fast processing and real time analysis. Solving big data analytics problems needs powerful platforms to handle this enormous mass of data and efficient machine learning algorithms to allow the use of big data full potential. Hidden Markov models are statistical models, rich and widely used in various fields especially for time varying data sequences modeling and analysis. They owe their success to the existence of many efficient and reliable algorithms. In this paper, we present ParaDist-HMM, a parallel distributed imple-mentation of hidden Markov model for modeling and solving big data analytics problems. We describe the development and the implementation of the improved algorithms and we propose a Spark-based approach consisting in a parallel distributed big data architecture in cloud computing environment, to put the proposed algorithms into practice. We evaluated the model on synthetic and real financial data in terms of running time, speedup and prediction quality which is measured by using the accuracy and the root mean square error. Experimental results demonstrate that ParaDist-HMM algorithms outperforms other implementations of hidden Markov models in terms of processing speed, accuracy and therefore in efficiency and effectiveness.
Keywords: Big data; machine learning; Hidden Markov model; forward; backward; baum-welch; parallel distributed computing; spark; cloud computing; ParaDist-HMM
Imad Sassi, Samir Anter and Abdelkrim Bekkhoucha, “ParaDist-HMM: A Parallel Distributed Implementation of Hidden Markov Model for Big Data Analytics using Spark” International Journal of Advanced Computer Science and Applications(IJACSA), 12(4), 2021. http://dx.doi.org/10.14569/IJACSA.2021.0120438
@article{Sassi2021,
title = {ParaDist-HMM: A Parallel Distributed Implementation of Hidden Markov Model for Big Data Analytics using Spark},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2021.0120438},
url = {http://dx.doi.org/10.14569/IJACSA.2021.0120438},
year = {2021},
publisher = {The Science and Information Organization},
volume = {12},
number = {4},
author = {Imad Sassi and Samir Anter and Abdelkrim Bekkhoucha}
}
