Assignment: Architecture in Health Care.
Big data analytics architecture in health care.
5Y. Wang et al. / Technological Forecasting & Social Change 126 (2018) 3–13
validated against data quality rules. Finally, the data are loaded into the target databases such as Hadoop distributed file systems (HDFS) or in a Hadoop cloud for further processing and analysis. The data storage prin- ciples are based on compliance regulations, data governance policies and access controls. Data storage methods can be implemented and completed in batch processes or in real time.
Analytics layer This layer is responsible for processing all kinds of data and
performing appropriate analyses. In this layer, data analysis can be di- vided into threemajor components: HadoopMap/Reduce, stream com- puting, and in-database analytics, depending on the type of data and the purpose of the analysis. Mapreduce is the most commonly used pro- grammingmodel in big data analytics which provides the ability to pro- cess large volumes of data in batch form cost-effectively, as well as allowing the analysis of both unstructured and structured data in amas- sively parallel processing (MPP) environment. Stream computing can support high performance stream data processing in near real time or real time. With a real time analysis, users can track data in motion, re- spond to unexpected events as they happen and quickly determine next-best actions. For example, in the case of healthcare fraud detection, stream computing is an important analytical tool that assists in predicting the likelihood of illegal transactions or deliberate misuse of customer accounts. Transactions and accounts will be analyzed in real time and alarms generated immediately to prevent myriad frauds across healthcare sectors. In-database analytics refers to a data mining approach built on an analytic platform that allows data to be processed within the datawarehouse. This component provides high-speed paral- lel processing, scalability, and optimization features geared toward big data analytics, and offers a secure environment for confidential enter- prise information. However, the results provided from in-database ana- lytics are neither current nor real time and it is therefore likely to generate reports with a static prediction. Typically, this analytic compo- nent in healthcare organizations is useful for supporting preventative healthcare practice and improving pharmaceutical management. The analytics layer also provides exceptional support for evidence based
medical practices by analyzing EHRs, patterns of care, care experience, and individual patients’ habits and medical histories.
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