(Publisher of Peer Reviewed Open Access Journals)

International Journal of Advanced Technology and Engineering Exploration (IJATEE)

ISSN (Print):2394-5443    ISSN (Online):2394-7454
Volume-5 Issue-48 November-2018
Full-Text PDF
DOI:10.19101/IJATEE.2018.547010
Paper Title : Harnessing supremacy of big data using hadoop for healthy human survival making use of bioinformatics
Author Name : Supreet Kaur and Seema Baghla
Abstract :

In this paper, the analysis of big data genre performed in order to achieve critical objectives for revolutionizing healthcare and to mine out the bioinformatics facet of a particular age group affected by a particular disease. The major challenge is to provide the right care, right living, and right value to general public by mining and providing available remedies for curing common and deadly diseases and it could be accomplished via appropriately mining the collected data. In the experimental work, data mining process was performed on the self-created primary database using Apache Hadoop framework and Hadoop based Hortonworks-Sandbox 2.2.0 data platform using the MapReduce algorithm. The result obtained describes that the scripts and queries provide sorted attributes from the database created and these attributes provide norms which justifies the objectives stated.

Keywords : Apache Hadoop, Hortonworks-sandbox 2.2.0, VMware player, File browser tool, HCatalog tool, Beeswax tool.
Cite this article : Supreet Kaur and Seema Baghla, " Harnessing supremacy of big data using hadoop for healthy human survival making use of bioinformatics " , International Journal of Advanced Technology and Engineering Exploration (IJATEE), Volume-5, Issue-48, November-2018 ,pp.460-468.DOI:10.19101/IJATEE.2018.547010
References :
[1]Luo J, Wu M, Gopukumar D, Zhao Y. Big data application in biomedical research and health care: a literature review. Biomedical Informatics Insights. 2016; 8: 1-10.
[Crossref] [Google Scholar]
[2]Kashyap H, Ahmed HA, Hoque N, Roy S, Bhattacharyya DK. Big data analytics in bioinformatics: architectures, techniques, tools and issues. Network Modeling Analysis in Health Informatics and Bioinformatics. 2016; 5(1).
[Crossref] [Google Scholar]
[3]Mukherjee A, Datta J, Jorapur R, Singhvi R, Haloi S, Akram W. Shared disk big data analytics with Apache Hadoop. In international conference on high performance computing 2012 (pp. 1-6). IEEE.
[Crossref] [Google Scholar]
[4]Tsai CF, Wu HC, Tsai CW. A new data clustering approach for data mining in large databases. In international symposium on parallel architectures, algorithms and networks 2002 (pp. 315-20). IEEE.
[Crossref] [Google Scholar]
[5]Dean J, Ghemawat S. MapReduce: simplified data processing on large clusters. Communications of the ACM. 2008; 51(1):107-13.
[Crossref] [Google Scholar]
[6]Greeshma L, Pradeepini G. Big data analytics with Apache Hadoop Mapreduce framework. Indian Journal of Science and Technology. 2016; 9(26):1-5.
[Crossref] [Google Scholar]
[7]Rane NP, Patil DD. Big data and big data security with Hadoop's MapReduce. International conference on natural computation 2014 (pp. 1508-13). IEEE.
[8]Khanal R. The role of open standard electronic health record in medical data mining. European Journal of Business Management and Research. 2017; 2(2):1-7.
[Crossref] [Google Scholar]
[9]Padhy S. Kumar S. Big data analysis using Apache Hadoop. International Journal of Advance Research, Ideas and Innovations in Technology. 2018; 4(1):225-7.
[Google Scholar]
[10]Saxena S, Kumar P, Tewari RG. Two-step technique for prediction analysis using k-means clustering algorithm. International Journal of Computer Applications. 2017; 166(9):9-12.
[Google Scholar]
[11]Shukla V, Dubey PK. Big Data: moving forward with emerging technology and challenges. International Journal of Advance Research in Computer Science and Management Studies. 2014; 2(9):187-93.
[Google Scholar]