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

Hidden  M 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...

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Veröffentlicht in:IAES International Journal of Artificial Intelligence Jg. 10; H. 3; S. 789
Hauptverfasser: Sassi, Imad, Anter, Samir, Bekkhoucha, Abdelkrim
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Yogyakarta IAES Institute of Advanced Engineering and Science 01.09.2021
Schlagworte:
Algorithms
Big Data
Data processing
Machine learning
Markov chains
Parallel processing
Polytopes
Run time (computers)
Sequences
ISSN:2089-4872, 2252-8938, 2089-4872
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Zusammenfassung:Hidden  M 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.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:2089-4872
2252-8938
2089-4872
DOI:10.11591/ijai.v10.i3.pp789-800