Approximative Event Processing on Sensor Data Streams (Best Poster and Demostration Award)

Conference contribution
(Original article)


Publication Details

Author(s): Löffler C, Mutschler C, Philippsen M
Publication year: 2015
Conference Proceedings Title: Proceedings of the 9th ACM International Conference on Distributed Event-Based Systems (DEBS'15)
Pages range: 360-363
ISBN: 978-1-4503-3286-6
Language: English


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.






 



FAU Authors / FAU Editors

Mutschler, Christopher Dr.-Ing.
Lehrstuhl für Informatik 2 (Programmiersysteme)
Philippsen, Michael Prof. Dr.
Lehrstuhl für Informatik 2 (Programmiersysteme)


How to cite

APA:
Löffler, C., Mutschler, C., & Philippsen, M. (2015). Approximative Event Processing on Sensor Data Streams (Best Poster and Demostration Award). In Proceedings of the 9th ACM International Conference on Distributed Event-Based Systems (DEBS'15) (pp. 360-363). Oslo, Norway, NO.

MLA:
Löffler, Christoffer, Christopher Mutschler, and Michael Philippsen. "Approximative Event Processing on Sensor Data Streams (Best Poster and Demostration Award)." Proceedings of the 9th ACM International Conference on Distributed Event-Based Systems (DEBS'15), Oslo, Norway 2015. 360-363.

BibTeX: 

Last updated on 2018-14-05 at 07:08