Context Awareness Technology Using Parallel Mining for Ambient Assisted Living System
-
2018-04-17 https://doi.org/10.14419/ijet.v7i2.19.15046
-
AAL, Big data, Cloud computing, Context management system, Rule induction -
Abstract
In this paper, context awareness is a promising technology that provides health care services and a niche area of big data paradigm. The  drift in Knowledge  Discovery from Data refers to a set of activities designed to refine and extract new knowledge from complex datasets. The  proposed model facilitates a parallel mining of frequent item sets for Ambient Assisted Living (AAL) System [a.k.a. Health Care [System] of big data that reside  inside a cloud environment. We extend a knowledge discovery framework for  processing and classifying the abnormal conditions of patients having fluctuations in Blood Pressure (BP) and Heart Rate(HR) and storing this data sets called Big data into Cloud to access from anywhere  when needed.  This  accessed data is used to compare the new data with it, which helps to know the patients health condition.
Â
Â
-
References
[1]. Vivek.J, Devasanthiya, C.Vigneshwari (2016), ‘An enhanced tourism recommendation system with relevancy feedback mechanism and ontological specifications’. Lakshmi, M.Refonaa, Vivek.J, ‘Tracking of bio medical waste using global positioning system’.
[2]. A.K. Dey (2000), ‘Providing Architectural Support for Building Context-Aware Applications’,Ph.D. dissertation, Georgia Institute of Technology.
[3]. Amazon web services https://aws.amazon.com
[4]. George Suciu, AlexandruVulpe, RazvanCraciunescu and Cristina Butca, Victor Suciu (2015), ‘Big Data Fusion for eHealth and Ambient Assisted Living Cloud Applications’, IEEE International Black Sea Conference on Communications and Networking, pp.102-106.
[5]. Mulvenna, M, Carswell, W, McCullagh, P, Augusto, J.C., Huiru Zhen, Jeffers, P., Haiying Wang and Martin, S(2011)., ‘Visualization of Data for Ambient Assisted Living Services’, in Communications Magazine, IEEE , Vol.49, No.1, pp.110-117.
[6]. P. R. Norris (2006), “Toward new vital signs: Tools and Mmethods for Physiologic Data Capture, Analysis, and Decision Support in Critical Care,†Ph.D. Dissertation, Biomedical Informatics, Vanderbilt University, Nashville, Tennessee, USA.
[7]. Suciu, G, Vulpe, A., Craciunescu, R., Butca, C and Suciu, V (2015) "Big data fusion for eHealth and Ambient Assisted Living Cloud Applications," IEEE International Black Sea Conference on Communications and Networkingpp.102-106, 18-21.
[8]. Venkatesh, V.; Vaithyanathan, V.; Kumar, M.P.; Raj, P.(2012), ‘A Secure Ambient Assisted Living (AAL) Environment: An implementation view,’ International Conference onComputer Communication and Informatics (ICCCI), pp.10
[9]. Vivek.J, Devasanthiya, C.Vigneshwari (2016), ‘An enhanced tourism recommendation system with relevancy feedback mechanism and ontological specifications’.
-
Downloads
-
How to Cite
Vivek, J., Maharnisha, G., Roopesh Kumar, G., Karun Sagar, C., & Arunraj, R. (2018). Context Awareness Technology Using Parallel Mining for Ambient Assisted Living System. International Journal of Engineering & Technology, 7(2.19), 52-54. https://doi.org/10.14419/ijet.v7i2.19.15046Received date: 2018-07-04
Accepted date: 2018-07-04
Published date: 2018-04-17