Failure-resilient real-time processing of health streams

Abstract

The ability to analyze streaming data in real time is vital in systems that process data from health sensors. These systems need to build and maintain state, as well as preserve this state during system failures. In this work, we introduce a fault-tolerance scheme designed for the Granules stream processing system. We work with two distinct health stream datasets: thorax extension and electroencephalogram (EEG) signal analysis. We have developed a monitoring program to track trends in the thorax extension dataset and a classification system for the EEG dataset, which allows us to determine user intent from EEG signals. Using these two motivating applications, we have explored several approaches to fault tolerance through replication, developing a hybrid approach that is particularly suited to health streams.