Stream processing of healthcare sensor data: Studying user traces to identify challenges from a big data perspective

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Abstract

The Internet of Things (IoT) generates massive streams of data which call for ever more efficient real time processing. Designing and implementing a big data service for the real time processing of such data requires an extensive knowledge of both input load and data distribution in order to provide a service which can cope with the workload. In this context, we study in this paper the challenges inherent to the real time processing of massive data flows from the IoT. We provide a detailed analysis of traces gathered from a well-known healthcare sport-oriented application in order to illustrate our conclusions from a big data perspective.

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APA

Cortés, R., Bonnaire, X., Marin, O., & Sens, P. (2015). Stream processing of healthcare sensor data: Studying user traces to identify challenges from a big data perspective. In Procedia Computer Science (Vol. 52, pp. 1004–1009). Elsevier B.V. https://doi.org/10.1016/j.procs.2015.05.093

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