الفهرس 
Healthcare Data Processing: An OverviewOnly 14 pages are availabe for public view
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Abstract
As expectations for the data processing platforms to support processing
for healthcare data, new services need to be deployed in healthcare services.
Different needs from healthcare organizations raised the importance
of the data to become reliable, high available, and high-performance
processing. Additionally, the security and privacy of patient data
have become a critical issue as a new need to keep health data secured
on public clouds. Today, big data processing plays an active role in performing
meaningful real-time analysis of the massive volume of health
data to predict emergencies. Therefore, a first motivation is to go beyond
the limitations of traditional data centres and provide a self-healing
framework for healthcare data centres. A second possible motivation is
to provide a framework for healthcare organizations to support real-time
processing for massive healthcare data. The last possible motivation is
enabling healthcare entities to use public cloud securely.
This thesis presents a novel framework, for self-healing of healthcare
data centres, and improving performance of healthcare data processing.
The performance of the framework is improved by using a multi-agent
architecture based on a distributed algorithm for data mining. Furthermore,
the self-healing healthcare data centre is improved by taking into
account reliability and availability during task execution jobs. Moreover,
this thesis presents an intelligent algorithm called IFHDS to secure health
data on a public cloud. IFHDS splits sensitive data into multiple parts
according to sensitivity level, where each part is stored separately over
distributed cloud storage. Finally, integrating all components in a novel
framework to solve one of the most critical challenges in healthcare era,
which is related to patient re-admission.