Comparing Real-time Data Analytics Technologies For Remote Patient Monitoring
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With the maturing of Big Data and telecommunication technologies, it has become possible to implement remote patient monitoring services for remote diagnosis. These services allow patients to be monitored regardless of their physical location. The more efficient these services are, the better the patients will be taken care of. Patients feel more secure with real-time monitoring because they know that they will receive an instant diagnosis when something anomalous happens in their bodies. The ease of developing and maintaining real-time data analytics technologies for remote patient monitoring services is a central factor in the development of real-time monitoring systems. An easy technical solution can help R&D teams to continuously deliver new versions of services to patients. Hence, patients can benefit from regularly updated service versions compared with traditional location-bound healthcare services. More complex technologies always requires more learning time and attention from developers. Therefore, selecting an easy programming technology can have a significant impact on providing real-time remote patient monitoring services. This thesis introduces three stream processing technologies that are popular both in the industry and academia. They all come from the open source Apache Foundation: Storm, Spark Streaming and Kafka Streams. The thesis first introduces the architecture and core concepts of the three technologies at high level. Then the author designs an experimental environment to compare the ease of programming and performance. Finally, the author studies the design philosophies of the three technologies and gives a detailed comparison of the internal implementations of the key features.