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Notice of Violation of IEEE Publication Principles
"On Medical Informatics for Pervasive and Ubiquitous Computing in eHealth"
by Aravind Kailas and Dimitrios Stefanidis
in the Proceedings of the 2012 International Conference on eHealth Networking, Applications and Services (HealthCom), October 2012, pp. 111-118
After careful and considered review of the content and authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE's Publication Principles.
This paper is a duplication of the original text from the paper cited below. The original text was copied without attribution (including appropriate references to the original author(s) and/or paper title) and without permission.
Due to the nature of this violation, reasonable effort should be made to remove all past references to this paper, and future references should be made to the following article:
"A Survey of Data Analytics Methods in Ubiquitous Sensor-Based Healthcare Information Systems"
by Arijit Mukherjee, Arpan Pal, Prateep Misra
in the Proceedings of the 2012 International Conference on Advances in Computing, Communications and Informatics (ICACCI), ACM, August 2012
As the world moves towards the reality of “intelligent infrastructures,” many avenues open up for research on sensor - based intelligent and ubiquitous systems. Healthcare is one such application area, where sensors and mobile platforms are becoming more useful and hence the idea of analyzing the data feeds from sensors to extract useful meanings is gaining in popularity. Various data-mining techniques are used in this regard. Apart from these, stream processing and continuous event processing are also becoming popular. This paper is a broad survey article where we look into the emerging trends in Ubiquitous Healthcare Information Systems, especially, various approaches taken in order to successfully use data analyti- s techniques on the data streams coming from the sensors and mobile platforms to cluster patients into similar groups, or analytics processing on streaming data to detect abnormal medical conditions as early as possible. The paper also refers to a recent research on non-parametric classification of data, which has the potential to discover interesting patterns within physiological data, which may otherwise remain undetected and advocates it's case in the health domain. Considering the size of the population and hence the volume of data, there are several architectural challenges such as scalability and availability of the platforms and handling of “big-data.” We try to summarize how these problems have been addressed and whether the solutions are adequate or not.