Abstract:It is always difficult to manipulate the production of huge amount of data which comes from multiple sources and to extract meaningful information to make appropriate decisions. When data comes from various input resources, to get required streams of events form this complex input network, the one of the strong functionality of Business Intelligence (BI) the Complex Event Processing (CEP) is the appropriate solution for the abovementioned problems. Real time processing, pattern matching, stream processing, big data management, sensor data processing and many more are the application areas of CEP. Health domain itself is a multidimension domain such as hospital supply chain, OPD management, disease diagnostic, Inpatient, out-patient management, and emergency care etc. In this paper, the main focus is to discuss the application areas of Complex Event Processing (CEP) in health domain by using sensor device, such that how CEP manipulate health data set events coming from sensor devices such as blood pressure, heartbeat, fall detection, sugar level, temperature or any other vital signs and how these systems respond to these events as quickly as possible. Different existing models and application using CEP are discussed and summarized according to different characteristics.
Hypertension, also known as Arterial Hypertension (HTN), is a long term and constant disease in which pressure of blood in the arteries is higher than the normal condition of its flow. Normally Blood pressure is measured by two levels i.e. Contraction (Systole) and relaxation (Diastole), these two are the maximum (Systolic) and minimum (Diastolic) blood pressure conditions. Hypertension can defaced the body organs and also lead many other illnesses and diseases like kidney failure, Heart attack and Heart failure etc. in middle age high rate of blood pressure may cause the severe decline in during the whole life time. For the purpose to calculate the spatial variation; to measure specific quantity at varies locations with the help of Descriptive Statistics by using Bayesian Model which reveals and identify some unique values and range of hospitalization admission for hypertension disease across Karachi city of Pakistan. This study of spatial variation of hospitalization admissions for hypertension disease is basically a framework which being developed for the specific purpose to control the high risk of hospital admissions in certain areas and aware about its varies rise and fall in the specific regions as well as provide useful information for concerned authorities and administrators in health department for its betterment and implementation to enhance the ease for hospital admission and reduce the rate of hypertension in certain age group. After this research further modification in the related title should explain the spatial patterns and in this regard should have some detailed and vast collection of Hypertension disease hospitalization records and data.[2][6] Figure 3: A Map of Karachi [1] www.en.wikipedia.org/wiki/Karachi.Information about Karachi
As, technology grows day by day ,computers become ever faster with its importance and having maximum computing power, computational cost also increases, development at its high end then we state that parallel computing technology (parallelism) is the main building block of this era of development and, for this purpose, therefore, all aspects have been thought about to meet the need of time.
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