Genome DNA Sequence Matching Using HBM Algorithm
The Legacy BM algorithm uses sequential pattern mining that search-relevant sequence patterns of a provided pattern from the dataset examples given as knowledgebase, also regarded as a statistical approach. The major issues that arise in such a proposal are identifying genome sequences first from the building databases and then after indexing those pattern statistics for sequence information. Furthermore, the extraction of frequently occurred patterns is a big task for the compilers when datasets are boundless. The third issue is comparing such sequences after the completion of prior phases for similarity and assumptions, making it easy for recovering missing sequence key pairs. To overcome these limitations of the BM pattern mining algorithm, we designed a parallel thread approach. Which in turn instead of following the parent algorithm breaks the loop into multiple small processes in just a milliseconds and then triggering each of the data objects in a single go, reducing the time consumption nearly to half of the linear approach. The suggested model will cover up the limitations of the BM algorithm and fills up a certain gap which are between the consecutive sequences variations.
Keywords: BM, KMP, Naïve, HBM, Sequence Matching