Mishra, Sitakanta, authorPallickara, Shrideep, advisorPallickara, Sangmi, committee memberVenkatachalam, Chandra, committee member2018-09-102018-09-102018https://hdl.handle.net/10217/191375Over the last decade, there has been an exponential growth in unbounded streaming data generated by sensing devices in different settings including the Internet-of-Things. Several frameworks have been developed to facilitate effective monitoring, processing, and analysis of the continuous flow of streams generated in such settings. Real-time data collected from patient monitoring systems, wearable devices etc. can take advantage of stream processing engines in distributed computing environments to provide better care and services to both individuals and medical practitioners. This thesis proposes a methodology for monitoring multiple users using stream data processing pipelines. We have designed data processing pipelines using the two dominant stream processing frameworks – Storm and Spark. We used the University of Queensland's Vital Sign Dataset in our assessments. Our assessments contrast these systems based on processing latencies, throughput, and also the number of concurrent users that can be supported in a given pipeline.born digitalmasters thesesengCopyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright.predictive analyticsstream processing enginesreal time data analysisInternet of thingsText