Distributed Search Of Biological Databases Using Hadoop/Mapreduce

dc.contributor.advisorStojkovic, Vojislav
dc.contributor.authorFashola, Babatunde Olaide
dc.contributor.departmentComputer Science and Bioinformatics Programen_US
dc.contributor.programMaster of Scienceen_US
dc.description.abstractThe main goals of this thesis research were to: 1. Make a computational platform/environment for thesis research. 2. Develop a MapReduce search algorithm that employs the scalability of a Hadoop cluster and the MapReduce functionalities to make the search of a biological database faster. 3. Implement the MapReduce search algorithm using the Java programming language, and running the consequent Java application in a Hadoop multi-node cluster in the cloud. 4. Compare execution times of - The MapReduce search program - The serial search programs – Boyer-Moore Algorithm and Knuth-Morris-Pratt Algorithm 13 GB of downloadable GenBank data was processed over the Hadoop framework installed on a 12-node cluster comprised of the Amazon EC2 t2.micro instance types. The execution time of the distributed search program is 46% faster than the execution times of the serial programs. Hence, the present search algorithms used for accessing the biological databases can incorporate the MapReduce programming model to improve their performances
dc.relation.isAvailableAtMorgan State University
dc.rightsThis item is made available by Morgan State University for personal, educational, and research purposes in accordance with Title 17 of the U.S. Copyright Law. Other uses may require permission from the copyright owner.
dc.subjectAmazon Web Services (Firm)en_US
dc.subjectComputer scienceen_US
dc.subjectCloud computingen_US
dc.titleDistributed Search Of Biological Databases Using Hadoop/Mapreduce