A spark-based parallel distributed posterior decoding algorithm for big data hidden Markov models decoding problem

Published on Sep 1, 2021in IAES International Journal of Artificial Intelligence
· DOI :10.11591/IJAI.V10.I3.PP789-800
Imad Sassi4
Estimated H-index: 4
,
Samir Anter5
Estimated H-index: 5
,
Abdelkrim Bekkhoucha2
Estimated H-index: 2
Sources
Abstract
Hidden  null M null arkov models (HMMs) are one of machine learning algorithms which have been widely used and demonstrated their efficiency in many conventional applications. This paper proposes a modified posterior decoding algorithm to solve hidden Markov models decoding problem based on MapReduce paradigm and spark’s resilient distributed dataset (RDDs) concept, for large-scale data processing. The objective of this work is to improve the performances of HMM to deal with big data challenges. The proposed algorithm shows a great improvement in reducing time complexity and provides good results in terms of running time, speedup, and parallelization efficiency for a large amount of data, i.e., large states number and large sequences number.
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