Big data stream computing in healthcare real-time analytics

Abstract

The healthcare industry is changing at a dramatic rate. There are multiple processes going on within the health sector. These processes not only impact the care of individuals but also help medical practitioners and the delivery of care and services. The industry can take advantage of big data analytics to ensure that all the multiple processes within the industry are running smoothly. Big data analytics is not just an opportunity but a necessity. Recently, big data stream computing has been studied in order to improve the quality of healthcare services and reduce costs by capability support prediction, thus making decisions in real-time. This paper proposes a generic architecture for big data healthcare analytic by using open sources, including Hadoop, Apache Storm, Kafka and NoSQL Cassandra. The combination of high throughput publish-subscribe messaging for streams, distributed real-time computing, and distributed storage system can effectively analyze a huge amount of healthcare data coming with a rapid rate.

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