ParaDist-HMM: A Parallel Distributed Implementation of Hidden Markov Model for Big Data Analytics using Spark

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...

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Bibliographic Details
Published in:International journal of advanced computer science & applications Vol. 12; no. 4
Main Authors: Sassi, Imad, Anter, Samir, Bekkhoucha, Abdelkrim
Format: Journal Article
Language:English
Published: West Yorkshire Science and Information (SAI) Organization Limited 2021
Subjects:
Accuracy
Algorithms
Big Data
Cloud computing
Data analysis
Machine learning
Markov chains
Mathematical analysis
Polytopes
Run time (computers)
Statistical models
ISSN:2158-107X, 2156-5570
Online Access:Get full text
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Summary: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.
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ISSN:2158-107X
2156-5570
DOI:10.14569/IJACSA.2021.0120438