A Novel Storage Architecture for Facilitating Efficient Analytics of Health Informatics Big Data in Cloud

Analytics of health big data are very crucial for providing cost effective quality health care. Over recent years, the analytics on healthcare big data has evolved into a challenging task for getting insights into a very large data set for improving the health services. This enormous amount of data, which is being generated incessantly over a long period of time, has put a great deal of stress on the write performance as well as on scalability. Moreover, there is a requirement of efficient storage and meaningful processing of these data which is an another challenging issue. The traditional relational databases, which were used in the storage of health data, are now unable to handle due to its massive and varied nature. Besides, these databases have some inherent weakness in terms of scalability, storing varied data format, etc. So there is a necessity for a new kind of data storage management system. This paper proposes a new big data storage architecture consisting of application cluster and a storage cluster to facilitate read/write/update speedup as well as data optimization. The application cluster is used to provide efficient storage and retrieval functions from the users. The storage services will be provided through the storage cluster.

[1]  Ilias Maglogiannis,et al.  Mobile healthcare information management utilizing Cloud Computing and Android OS , 2010, 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology.

[2]  Albert Y. Zomaya,et al.  Software Tools and Techniques for Big Data Computing in Healthcare Clouds , 2015, Future Gener. Comput. Syst..

[3]  Frank J. Ohlhorst Big Data Analytics: Turning Big Data into Big Money , 2012 .

[4]  Miloš Milovanović,et al.  Cloud Based Metalearning System for Predictive Modeling of Biomedical Data , 2014, TheScientificWorldJournal.

[5]  Amir Esmailpour,et al.  A Hybrid Data Center Architecture for Big Data , 2016, Big Data Res..

[6]  Paul Zikopoulos,et al.  Understanding Big Data: Analytics for Enterprise Class Hadoop and Streaming Data , 2011 .

[7]  G. Cuckler,et al.  National health expenditure projections, 2012-22: slow growth until coverage expands and economy improves. , 2013, Health affairs.

[8]  Chen Li,et al.  Big data platforms: What's next? , 2012, XRDS.

[9]  D. Lazer,et al.  The Parable of Google Flu: Traps in Big Data Analysis , 2014, Science.

[10]  Viju Raghupathi,et al.  Big data analytics in healthcare: promise and potential , 2014, Health Information Science and Systems.

[11]  Vaibhav Kohli,et al.  Big Data Processing using Apache Hadoop in Cloud System , 2012 .

[12]  Neal Leavitt,et al.  Will NoSQL Databases Live Up to Their Promise? , 2010, Computer.

[13]  Sooyoung Yoo,et al.  Architecture Design of Healthcare Software-as-a-Service Platform for Cloud-Based Clinical Decision Support Service , 2015, Healthcare informatics research.

[14]  Victor I. Chang,et al.  A model to compare cloud and non-cloud storage of Big Data , 2016, Future Gener. Comput. Syst..

[15]  Kup-Sze Choi,et al.  Alternatives to relational database: Comparison of NoSQL and XML approaches for clinical data storage , 2013, Comput. Methods Programs Biomed..

[16]  Roy D. Sleator,et al.  'Big data', Hadoop and cloud computing in genomics , 2013, J. Biomed. Informatics.

[17]  B. Saleena,et al.  Designing a Cloud Based Framework for HealthCare System and Applying Clustering Techniques for Region Wise Diagnosis , 2015 .

[18]  Athanasios V. Vasilakos,et al.  Big data: From beginning to future , 2016, Int. J. Inf. Manag..

[19]  Martin Schmitz,et al.  Scalable Predictive Analysis in Critically Ill Patients Using a Visual Open Data Analysis Platform , 2016, PloS one.

[20]  Jin Zhang,et al.  Xbase: cloud-enabled information appliance for healthcare , 2010, EDBT '10.