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Approximative event processing on sensor data streams

 
: Löffler, Christoffer; Mutschler, Christopher; Philippen, Michael

:

Association for Computing Machinery -ACM-; Association for Computing Machinery -ACM-, Special Interest Group on Management of Data -SIGMOD-; Association for Computing Machinery -ACM-, Special Interest Group on Software Engineering -SIGSOFT-:
DEBS 2015, 9th ACM International Conference on Distributed Event-Based Systems. Proceedings : Oslo, Norway, June 29 - July 3, 2015
New York: ACM, 2015
ISBN: 978-1-4503-3286-6
pp.360-363
International Conference on Distributed Event-Based Systems (DEBS) <9, 2015, Oslo>
English
Conference Paper
Fraunhofer IIS ()
ereignisgesteuerte Programmierung

Abstract
Event-Based Systems (EBS) can efficiently analyze large streams of sensor data in near-realtime. But they struggle with noise or incompleteness that is seen in the unprecedented amount of data generated by the Internet of Things. We present a generic approach that deals with uncertain data in the middleware layer of distributed event-based systems and is hence transparent for developers. Our approach calculates alternative paths to improve the overall result of the data analysis. It dynamically generates, updates, and evaluates Bayesian Networks based on probability measures and rules defined by developers. An evaluation on position data shows that the improved detection rate justifies the computational overhead.

: http://publica.fraunhofer.de/documents/N-369318.html