International Journal of Advanced Networking and Applications

Open Access Open Access  Restricted Access Subscription Access

Significance of Big Data Frameworks and Speculative Approaches in Healthcare Systems


Affiliations
1 Department of Information Science Engineering, SDM Institute of Technology, Ujire, Mangalore, India
2 Department of Computer Science and Engineering, VCE, Hyderabad, India
 

Due to rapid generation of large numbers of integrated medical data from various communities of the world, health care systems need to be stored and streamed properly. Heterogeneous data has been scattered from different healthcare medical records has various attributes and primarily they are not structured using data frameworks. In this paper we emphasized the various frameworks of big data and its significance in healthcare systems. This paper also focuses on the significance of speculative approaches and the streaming process of data frameworks. This would be helpful for researchers to analyse and evaluate the characteristics of frameworks with respect to network throughput and latency. Selection of nodes for different stages of healthcare is also a challenging issue while selecting data frameworks. State of art approaches show the role of big data frameworks in other sectors of applications.

Keywords

Big data, pig, spark, flume, frameworks, Hadoop
User
Notifications
Font Size

  • S. Agarwal state of fast data and streaming applications survey, 2017.

  • M. Mohammadi and A. Al-Fuqaha, “Enabling Cognitive Smart Cities Using Big Data and Machine Learning: Approaches and Challenges,” IEEE Communications Magazine, vol. 56, no. 2, pp. 94– 101, 2018.

  • Gani, A., Siddiqa, A., Shamshirband, S., &Hanum, F.: A survey on indexing techniques for big data: taxonomy and performance evaluation. Knowledge and Information Systems, 46(2), 241–284, 2016.

  • Gubbi, J., Buyya, R., Marusic, S., &Palaniswami, M.: Internet of Things (IoT): a vision, architectural elements, and future directions. Future Generation Computer Systems, 29(7), 1645– 1660, 2013.

  • A. Zanella, N. Bui, A. Castellani, L. Vangelista, and M. Zorzi, “Internet of things for smart cities,” IEEE Internet of Things Journal, vol. 1, no. 1, pp. 22–32, 2014.

  • H. Isah, T. Abughofa, S. Mahfuz, D. Ajerla, F. Zulkernine and S. Khan, "A Survey of Distributed Data Stream Processing Frameworks," in IEEE Access, vol. 7, pp. 154300-154316, 2019.

  • Manovich L, Trending: the promises and the challenges of big social data. In: Gold MK (ed) Debates in the digital humanities. University of Minessota Press, Minneapolis, pp 460–475, 2012.

  • Burgess J, Bruns A Twitter archives and the challenges of “Big Social Data” for media and communication research. M/C J 15(5), 1–7, 2012.

  • U. Khan, J. P. Choi, H. Shin, and M. Kim, “Predicting breast cancer survivability using fuzzy decision trees for personalized healthcare,” in Engineering in Medicine and Biology Society, 2008. EMBS 2008. 30th Annual International Conference of the IEEE, 2008, pp. 5148–5151.

  • A.Andreescu et al., “Empirically derived decision trees for the treatment of late-life depression,” American Journal of Psychiatry, vol. 165, no. 7, pp. 855–862, 2008.

  • M. Shouman, T. Turner, and R. Stocker, “Applying k-nearest neighbour in diagnosing heart disease patients,” International Journal of Information and Education Technology, vol. 2, no. 3, pp. 220–223, 2012.

  • M. P. Brown et al., “Knowledge-based analysis of microarray gene expression data by using support vector machines,” Proceedings of the National Academy of Sciences, vol. 97, no. 1, pp. 262–267, 2000.

  • Barakat, A. P. Bradley, and M. N. H. Barakat, “Intelligible support vector machines for diagnosis of diabetes mellitus,” IEEE transactions on information technology in biomedicine, vol. 14, no. 4, pp. 1114– 1120, 2010.

  • T. J. Hannan, “Electronic medical records,” Health informatics: An overview, vol. 133, 1996.

  • X. Fei, X. Li, and C. Shen, “Parallelized text classification algorithm for processing large-scale TCM clinical data with MapReduce,” in Information and Automation, 2015 IEEE International Conference on, 2015, pp. 1983–1986.

  • M. Bittorf et al., “Impala: A modern, open-source SQL engine for Hadoop,” in Proceedings of the 7th Biennial Conference on Innovative Data Systems Research, 2015.

  • R. Lin, Z. Ye, H. Wang, and B. Wu, “Chronic Diseases and Health Monitoring Big Data: A Survey,” IEEE Reviews in Biomedical Engineering, 2018.

  • T. Jones, “Process real-time big data with Twitter Storm,” IBM Technical Library, 2013.

  • M. Zaharia et al., “Apache spark: a unified engine for big data processing,” Communications of the ACM, vol. 59, no. 11, pp. 56–65, 2016.

  • R. Lin, Z. Ye, H. Wang, and B. Wu, “Chronic Diseases and Health Monitoring Big Data: A Survey,” IEEE Reviews in Biomedical Engineering, 2018.

  • D. Lyubimov and A. Palumbo, Apache Mahout: Beyond MapReduce. CreateSpace Independent Publishing Platform, 2016.

  • A.Thusoo et al., “Hive: a warehousing solution over a map-reduce framework,” Proceedings of the VLDB Endowment, vol. 2, no. 2, pp. 1626–1629, 2009.

  • Marco Viceconti, Peter Hunter and Rod Hose, "Big Data Big Knowledge: Big Data for Personalized Healthcare", IEEE Journal of Biomedical and Health Informatics, vol. 19, no. 4, pp. 1209-1215, 2015.

  • Naoual El aboudi and Laila Benhlima, "Big Data Management for Healthcare Systems: Architecture Requirements and Implementation", Advances in Bioinformatics, pp. 1-10, 2018.

  • Yichuan Wanga, LeeAnn Kungb, William Yu Chung Wangc and Casey G. Cegielskid, "An Integrated Big Data Analytics-Enabled Transformation Model: Application to Health care", Elsevier Journal of Information & Management, vol. 55, pp. 64-79, 2018.

  • Sari I. Lakkis, Maher Elshakankiri, "IoT based emergency and operational services in medical care systems", Internet of Things Business Models Users and Networks 2017, pp. 1-5, 2017.

  • B.Thillaieswari, “Comparative Study on Tools and Techniques of Big Data Analysis”, International Journal of Advanced Networking & Applications (IJANA) Volume: 08, Issue: 05 Pages: 61-66, 2017.

  • B.Prasanna, A.Prema, K.Chelladurai , “E-Health for Security and Privacy in Health Care System Using Hadoop Map Reduce”, International Journal of Advanced Networking & Applications (IJANA) Volume: 08, Issue: 05 Pages: 101-104 , 2017.


Abstract Views: 221

PDF Views: 1




  • Significance of Big Data Frameworks and Speculative Approaches in Healthcare Systems

Abstract Views: 221  |  PDF Views: 1

Authors

G.P. Hegde
Department of Information Science Engineering, SDM Institute of Technology, Ujire, Mangalore, India
Nagaratna Hegde
Department of Computer Science and Engineering, VCE, Hyderabad, India

Abstract


Due to rapid generation of large numbers of integrated medical data from various communities of the world, health care systems need to be stored and streamed properly. Heterogeneous data has been scattered from different healthcare medical records has various attributes and primarily they are not structured using data frameworks. In this paper we emphasized the various frameworks of big data and its significance in healthcare systems. This paper also focuses on the significance of speculative approaches and the streaming process of data frameworks. This would be helpful for researchers to analyse and evaluate the characteristics of frameworks with respect to network throughput and latency. Selection of nodes for different stages of healthcare is also a challenging issue while selecting data frameworks. State of art approaches show the role of big data frameworks in other sectors of applications.

Keywords


Big data, pig, spark, flume, frameworks, Hadoop

References