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

<span lang="EN-US">Hidden </span><span lang="IN">M</span><span lang="EN-US">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 problem based on MapReduce paradigm spark’s resilient distributed dataset (RDDs) concept, for large-scale data processing. The objective this work is improve the performances HMM deal with big challenges. proposed shows great improvement reducing time complexity provides good results terms running time, speedup, parallelization large amount data, i.e., states number sequences number.</span>