Home About Login Paper submission Search Current Cross Ref Book Contact Archive Announcement

Big Data and its application in Biomedical Domain

Editor IJSMI


Big data [1,2,3]  redefined the analytical space with its features such as Volume, Velocity, Variety, Veracity and Value. Due to advancement in the technology, storing, retrieving of data is becoming easier than in the previous decades. Due to this fact, the volume of data in the form of text, image, sound is increasing at a rapid pace in all the fields including the biomedical field [4, 5]. Patient records in the form of electronic medical records are being stored in the data cloud which includes data in different forms. To analyse the data of such magnitude and variety, traditional analytical tools and methods are not sufficient. The Big Data analytical techniques help to analyse these kinds of data and helps us to arrive at decisions quickly. Managing the privacy, security and government regulations related to patient data remains as a challenge [6] in implementing Big Data analytical tools in biomedical domain. This paper starts with the overview of Big Data architecture and moves on to explaining the tools and technologies used in Big Data and the uses of Big Data in Biomedical Field.


Big Data, Hadoop, MapReduce, Hive, HBase, Storm, Spark

Full Text:



Zikopoulos, P., & Eaton, C. (2011). Understanding big data: Analytics for enterprise class hadoop and streaming data. McGraw-Hill Osborne Media.

Kambatla, K., Kollias, G., Kumar, V., & Grama, A. (2014). Trends in big data analytics. Journal of Parallel and Distributed Computing, 74(7), 2561-2573.

Gandomi, A., & Haider, M. (2015). Beyond the hype: Big data concepts, methods, and analytics. International Journal of Information Management, 35(2), 137-144.

Wang, Y., Kung, L., & Byrd, T. A. (2018). Big data analytics: Understanding its capabilities and potential benefits for healthcare organizations. Technological Forecasting and Social Change, 126, 3-13.

Murdoch, T. B., & Detsky, A. S. (2013). The inevitable application of big data to health care. Jama, 309(13), 1351-1352.

Huang, T., Lan, L., Fang, X., An, P., Min, J., & Wang, F. (2015). Promises and challenges of big data computing in health sciences. Big Data Research, 2(1), 2-11.

Hu, H., Wen, Y., Chua, T. S., & Li, X. (2014). Toward scalable systems for big data analytics: A technology tutorial. IEEE access, 2, 652-687.







Pokorny, J. (2013). NoSQL databases: a step to database scalability in web environment. International Journal of Web Information Systems, 9(1), 69-82.

Editor, IJSMI (2018), Machine Learning: An overview with the help of R software, ISBN 978-1729293577

Editor, IJSMI (2018). Application of statistical tools in biomedical domain: An overview with help of software. ISBN 9781986988551.

Bates, D. W., Saria, S., Ohno-Machado, L., Shah, A., & Escobar, G. (2014). Big data in health care: using analytics to identify and manage high-risk and high-cost patients. Health Affairs, 33(7), 1123-1131.

Costa, F. F. (2014). Big data in biomedicine. Drug discovery today, 19(4), 433-440.

Sun, J., & Reddy, C. K. (2013, August). Big data analytics for healthcare. In Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 1525-1525). ACM.

Kuo, M. H., Sahama, T., Kushniruk, A. W., Borycki, E. M., & Grunwell, D. K. (2014). Health big data analytics: current perspectives, challenges and potential solutions. International Journal of Big Data Intelligence, 1(1-2), 114-126.

DOI: http://dx.doi.org/10.3000/ijsmi.v9i1.14

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.