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Failure‐resilient real‐time processing of health streams
Author(s) -
Ericson Kathleen,
Pallickara Shrideep,
Anderson Charles W.
Publication year - 2015
Publication title -
concurrency and computation: practice and experience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.309
H-Index - 67
eISSN - 1532-0634
pISSN - 1532-0626
DOI - 10.1002/cpe.3324
Subject(s) - computer science , data stream mining , stream processing , fault tolerance , replication (statistics) , process (computing) , real time computing , state (computer science) , streams , data mining , extension (predicate logic) , artificial intelligence , distributed computing , computer network , programming language , statistics , mathematics
Summary 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. Copyright © 2014 John Wiley & Sons, Ltd.