Stream Processing of Healthcare Sensor Data: Studying User Traces to Identify Challenges from a Big Data Perspective

By Rudyar Cortés, Xavier Bonnaire, O. Marin, Pierre Sens. ANT/SEIT, 2015

331 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Management Information Systems, Signal Processing

View Paper: https://www.semanticscholar.org/paper/e01b3606e036adc406aec8d98ec7b6c3f9112ab2

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. L. Xing
2. Angela Zhang
3. James Y. Zou
4. Rajkumar Buyya
5. Joseph C. Wu
6. Sunipa Dev
7. Weixin Liang
8. Kai-Wei Chang

In Management Information Systems

1. Rajkumar Buyya

In Signal Processing

1. James Y. Zou
2. Rajkumar Buyya
3. Harshit Gupta

Citations in the corpus

• Shifting machine learning for healthcare from development to deployment and from models to data (2022, methodology): “In particular, handling peaks of data generation may require the design and implementation of scalable ways to support a variable number of connected objects 157 .”
• Fog Computing: Principles, Architectures, and Applications (2016, methodology): “2 Motivation Scenario A recent analysis [18] of Endomondo application, a popular sport activity tracking application has revealed number of remarkable observations.”
• Survey on fog computing: architecture, key technologies, applications and open issues (2017, background): “So the work pattern that transfers data to cloud and back to the client is unacceptable for latency-sensitive and real-time applications, such as health-monitoring (Cortes et al., 2015), emergency-response, etc.”