HCloud, a Healthcare-Oriented Cloud System with Improved Efficiency in Biomedical Data Processing - Cloud Computing with e-Science Applications - page 190

at any time and in any place. Through the system, anyone can have knowl-
edge regarding personal health information and even be told the risk of some
chronic diseases in the future. With our system, some acute attacks can be dis-
covered in time, and chronic diseases such as hypertension can be prevented
before their onset. This chapter proposed a private cloud platform architec-
ture associated with technologies such as MQ, load balance, session cache,
and cloud storage. This platform can integrate semistructured, unstructured,
and heterogeneous physiological signal data well and can support huge data
storage and heterogeneous data processing for various health care applica-
tions, such as automated ECG analysis, PPG analysis, and HBP analysis.
It is also a low-cost solution that can reduce module coupling by adopting
component technology. Moreover, the proposed system can provide an early
warning mechanism for people with chronic diseases and help physicians
obtain patients' health information. The Map-Reduce paradigm has the fea-
tures of code simplicity, data locality, and automatic parallelization compared
with other distributed parallel systems. More important, integrated with the
HCloud is improved efficiency of physiological data processing and achieve-
ment of linear speed-up. Based on the performance evaluation and feedback
from user experiences, HCloud can cope with the issues of high concurrent
requests in ubiquitous health care service and dispose of the analysis of
massive physiological signal tasks quickly, as well as having robust, instant,
and efficient features that can meet user demands for preventive health care.
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