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Authors: Jonathan Boidol 1 and Andreas Hapfelmeier 2

Affiliations: 1 Ludwig-Maximilians University and Siemens AG, Germany ; 2 Siemens AG, Germany

ISBN: 978-989-758-183-0

Keyword(s): Sensor Application, Online Algorithm, Entropy-based Correlation Analysis.

Abstract: Intelligent production in smart factories or wearable devices that measure our activities produce on an ever growing amount of sensor data. In these environments, the validation of measurements to distinguish sensor flukes from significant events is of particular importance. We developed an algorithm that detects dependencies between sensor readings. These can be used for instance to verify or analyze large scale measurements. An entropy based approach allows us to detect dependencies beyond linear correlation and is well suited to deal with high dimensional and high volume data streams. Results show statistically significant improvements in reliability and on-par execution time over other stream monitoring systems.

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Paper citation in several formats:
Boidol, J.; Boidol, J. and Hapfelmeier, A. (2016). Detecting Data Stream Dependencies on High Dimensional Data.In Proceedings of the International Conference on Internet of Things and Big Data - Volume 1: IoTBD, ISBN 978-989-758-183-0, pages 383-390. DOI: 10.5220/0005953303830390

@conference{iotbd16,
author={Jonathan Boidol. and Jonathan Boidol. and Andreas Hapfelmeier.},
title={Detecting Data Stream Dependencies on High Dimensional Data},
booktitle={Proceedings of the International Conference on Internet of Things and Big Data - Volume 1: IoTBD,},
year={2016},
pages={383-390},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005953303830390},
isbn={978-989-758-183-0},
}

TY - CONF

JO - Proceedings of the International Conference on Internet of Things and Big Data - Volume 1: IoTBD,
TI - Detecting Data Stream Dependencies on High Dimensional Data
SN - 978-989-758-183-0
AU - Boidol, J.
AU - Boidol, J.
AU - Hapfelmeier, A.
PY - 2016
SP - 383
EP - 390
DO - 10.5220/0005953303830390

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