Indian Health Care Analysis using Big Data Programming Tool

Abstract Big Data in layman terms means lots of data. This enormous amount of data is used for analysis and research purposes. This data accounts for trends in various domains. Organizations are using Big Data to predict the future in turn making them smarter and efficient. Applications from Big Data are innumerable, from retail industry where Big Data helps retailers gain insights into the customer to needs and habits, to Banking, HealthCare & Hospitality. Government agencies are increasingly incorporating Big Data analytics to curb crime and maintain law and order through social media traffic analysis and other means. Therefore, to get actionable data and perform analytics requires specialized tools. There are thousands of Big Data tools available in the market. There are open source tools like Hadoop, a term which has become synonymous to Big Data. In this paper, we authors analyzed the health care dataset against different research queries using Pig Latin Script, over the last few decades; quality of health care services in India has been improved tremendously because of the improved health care services, increased number of private and government hospitals and increased number of doctors with recognized medical qualification. In spite of significant growth government has to take strict measures to improve the overall health care facilities in India because considerable amount of gaps do exist between the demands to that of quality supply of healthcare services.

[1]  Hai Jin,et al.  Evaluating MapReduce on Virtual Machines: The Hadoop Case , 2009, CloudCom.

[2]  Y Al-JarrahOmar,et al.  Efficient Machine Learning for Big Data , 2015 .

[3]  Adam Jacobs,et al.  The pathologies of big data , 2009, Commun. ACM.

[4]  Tao Huang,et al.  Promises and Challenges of Big Data Computing in Health Sciences , 2015, Big Data Res..

[5]  Aboul Ella Hassanien,et al.  Dimensionality reduction of medical big data using neural-fuzzy classifier , 2014, Soft Computing.

[6]  Benjamin W. Wah,et al.  Significance and Challenges of Big Data Research , 2015, Big Data Res..

[7]  GaniAbdullah,et al.  The rise of "big data" on cloud computing , 2015 .

[8]  George K. Karagiannidis,et al.  Efficient Machine Learning for Big Data: A Review , 2015, Big Data Res..

[9]  Jemal H. Abawajy,et al.  Big Data in Complex Systems: Challenges and Opportunities , 2015 .

[10]  Yunhao Liu,et al.  Big Data: A Survey , 2014, Mob. Networks Appl..

[11]  Dominika Tkaczyk,et al.  Large scale citation matching using Apache Hadoop , 2013, TPDL.

[12]  H. V. Jagadish Big Data and Science: Myths and Reality , 2015, Big Data Res..

[13]  Stathes Hadjiefthymiades,et al.  An Efficient Time Optimized Scheme for Progressive Analytics in Big Data , 2015, Big Data Res..

[14]  N. B. Anuar,et al.  The rise of "big data" on cloud computing: Review and open research issues , 2015, Inf. Syst..

[15]  Thandar Thein,et al.  A platform for big data analytics on distributed scale-out storage system , 2015, Int. J. Big Data Intell..