Detecting Data Stream Dependencies on High Dimensional Data

Jonathan Boidol, Andreas Hapfelmeier

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 Harvard Style

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


in Bibtex Style

@conference{iotbd16,
author={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},
}


in EndNote Style

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 - Hapfelmeier A.
PY - 2016
SP - 383
EP - 390
DO - 10.5220/0005953303830390