Abstract
A large amount of modern healthcare data is generated through imaging, Electronic Health Report (EHR), sensor based technology and other various healthcare processes. An elaborative perspective in technological advancement has enabled practitioners to answer questions for governance and future decision making. However, very few tools exist to critically analyze such big data for future knowledge discovery. We can further say that cloud computing technology can be a benchmark to substantiate big data which may lead to discover of hidden patterns and trends to enhance knowledge for progression of disease. This paper approached various aspects of cloud based services to enable big data analytic in healthcare data management system.
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Mashey, J.: Big data and the next wave of infrastress. In: UseNIX Technical Conference (1999). http://wwwUsemix.org/publications/library/proceedings/usemix99/invited.talks/mashey.pdf
Weiss, S.H., Indurkhya, N.: Predictive Data Mining: A Practical Guide. Morgan Kaufmann Publishers, San Francisco (1998)
Xindong, W., Gong-Quing, W., Ding, W.: Data mining with big data. IEEE Trans. Knowl. Data Eng. 26(1), 97–107 (2014)
Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning, p. 533. Springer, Heidelberg (2001). ISBN 9780387848587
Demchenko, Y., Zhao, Z., Grosso, P., Wibisono, A., Laat, C.: Addressing big data challenges for scientific data infrastructure. In: IEEE 4th International Conference on Cloud Computing Technology and Science (CloudCom 2012), Picatawaj, NJ, pp. 614–617. IEEE (2012)
Chauhan, R., Kaur, H., Alam, A.: Data clustering method for discovering clusters in spatial cancer databases. Int. J. Comput. Appl. 10(6), 9–14 (2010)
Manyika, J., Chui, M., Brown, B., Buhin, J., Dobbs, R., Roxburgh, C., Byers, A.H.: Big Data: The Next Frontier for Innovation, Competition, and Productivity. McKinsey Global Institute, USA, pp. 1–36 (2011)
Duhigg, C.: The power of habit: why we do what we do in life and business, p. 416. Random House, New York, William Heinemann, London (2012)
Hellerstein, J.: Parallel Programming in the Age of Big Data. Gigaom Blog (2008). http://gigaom.com/2008/11/09/mapreduce-leads-the-way-for-parallel-programming/
Ursum, J., Bos, W.H., van de Stadt, R.J., Dijkmans, B.A., van Schaardenburg, D.: Different properties of ACPA and IgM-RF derived from a large dataset: further evidence of two distinct autoantibody systems. Arthritis Res. Ther. 2009 11(3), 1439–1443 (2009)
Jacobs, A.: The Pathologies of Big Data. ACM Queue 7(6), 10 (2009)
Ajdacic-Gross, V., Vetter, S., Müller, M., Kawohl, W., Frey, F., Lupi, G., Blechschmidt, A., Born, C., Latal, B., Rössler, W.: Risk factors for stuttering: a secondary analysis of a large data base. Eur. Arch. Psychiatry Clin. Neurosci. 260(4), 279–286 (2010)
Bunch, C., Chohan, N., Krintz, C., Chohan, J., Kupferman, J., Lakhina, P., Li, Y., Nomura, Y.: An evaluation of distributed datastores using the appscale cloud platform. In: Proceedings of the 3rd IEEE International Conference on Cloud Computing (Cloud 2010), pp. 305–312. IEEE Computer Society, Washington (2010)
Buyya, R., Yeo, C.S., Venugopal, S., Broberg, J., Brandic, I.: Cloud computing and emerging IT platforms: vision, hype, and reality for delivering computing as the 5th utility. Future Gener. Comput. Syst. 25(6), 599–616 (2009)
Calheiros, R.N., Vecchiola, C., Karunamoorthy, D., Buyya, R.: The Aneka platform and QoS-driven resource provisioning for elastic applications on hybrid Clouds. Future Gener. Comput. Syst. 28(6), 861–870 (2012)
Kaur, H., Chauhan, R., Wasan, S.K.: A Bayesian network model for probability estimation. In: Khosrow-Pour, M. (ed.) Encyclopedia of Information Science and Technology, 3rd edn. pp. 1551–1558 (2015). Accessed 10 Dec 2014. doi:10.4018/978-1-4666-5888-2.ch148 (2014)
Chauhan, R., Kaur, H.: Big data application in medical domain. In: Computational Intelligence for Big Data Analysis: Frontier Advances and Applications. Adaptation, Learning, and Optimization, vol. 19, pp. 165–179. Springer International Publishing, Switzerland (2015)
Chauhan, R., Kaur, H.: SPAM: an effective and efficient spatial algorithm for mining grid data. In: Geo-Intelligence and Visualization through Big Data Trends, pp. 245–263. IGI Global (2015). Web 9 September 2015. doi:10.4018/978-1-4666-8465-2.ch010
Kaur, H., Chauhan, R., Alam, M.A.: SPAGRID: a spatial grid framework for medical high dimensional databases. In: Proceedings of International Conference on Hybrid Artificial Intelligence Systems, HAIS 2012, vol. 1, pp. 690–704. Springer (2012)
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Chauhan, R., Jangade, R., Mudunuru, V.K. (2018). A Cloud Based Environment for Big Data Analytics in Healthcare. In: Abraham, A., Cherukuri, A., Madureira, A., Muda, A. (eds) Proceedings of the Eighth International Conference on Soft Computing and Pattern Recognition (SoCPaR 2016). SoCPaR 2016. Advances in Intelligent Systems and Computing, vol 614. Springer, Cham. https://doi.org/10.1007/978-3-319-60618-7_31
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DOI: https://doi.org/10.1007/978-3-319-60618-7_31
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