A MAP REDUCE MODEL TO FIND LONGEST COMMON SUBSEQUENCE USING NON-ALIGNMENT BASED APPROACH
| dc.contributor.author | KANDEL, NARAYAN PRASAD | |
| dc.date.accessioned | 2022-01-24T06:52:20Z | |
| dc.date.available | 2022-01-24T06:52:20Z | |
| dc.date.issued | 2016-10 | |
| dc.description | Biological sequences Longest Common Subsequence (LCS) identification has significant applications in bioinformatics. | en_US |
| dc.description.abstract | Biological sequences Longest Common Subsequence (LCS) identification has significant applications in bioinformatics. Due to the emerging growth of bioinformatics applications, new biological sequences with longer length have been used for processing, making it a great challenge for sequential LCS algorithms. Few parallel LCS algorithms have been proposed but their efficiency and effectiveness are not satisfactory with increasing complexity and size of the biological data. An non-alignment based method of sequence comparison using single layer map reduce based scalable parallel algorithm is presented with some optimization for computing LCS between genetic sequences. | en_US |
| dc.identifier.citation | MASTER OF SCIENCE IN COMPUTER SYSTEM AND KNOWLEDGE ENGINEERING | en_US |
| dc.identifier.uri | https://hdl.handle.net/20.500.14540/7646 | |
| dc.language.iso | en | en_US |
| dc.publisher | Pulchowk Campus | en_US |
| dc.subject | Bioinformatics, Longest Common Subsequence, | en_US |
| dc.subject | MapReduce, Hadoop | en_US |
| dc.title | A MAP REDUCE MODEL TO FIND LONGEST COMMON SUBSEQUENCE USING NON-ALIGNMENT BASED APPROACH | en_US |
| dc.type | Thesis | en_US |
| local.academic.level | Masters | en_US |
| local.affiliatedinstitute.title | Pulchowk Campus | en_US |
| local.institute.title | Institute of Engineering | en_US |