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Authors: Tristan Langer and Tobias Meisen

Affiliation: Chair of Technologies and Management of Digital Transformation, University of Wuppertal, Rainer-Gruenter-Str. 21, 42119 Wuppertal, Germany

Keyword(s): Time Series Analysis, Industrial Sensor Data, Visual Analytics, Analytic Provenance.

Abstract: Due to the increasing digitalization of production processes, more and more sensor data is recorded for subsequent analysis in various use cases (e.g. predictive maintenance). The analysis and utilization of this data by process experts raises optimization potential throughout the production process. However, new analysis methods are usually first published as non-standardized Python or R libraries and are therefore not available to process experts with limited programming and data management knowledge. It often takes years before those methods are used in ERP, MES and other production environments and the optimization potential remains idle until then. In this paper, we present a visual analytics approach to facilitate the inclusion of process experts into analysis and utilization of industrial sensor data. Based on two real world exemplary use cases, we define a catalog of requirements and develop a tool that provides dedicated interactive visualizations along methods for ex ploration, clustering and labeling as well as classification of sensor data. We then evaluate the usefulness of the presented tool in a qualitative user study. The feedback given by the participants indicates that such an approach eases access to data analysis methods but needs to be integrated into a comprehensive data management and analysis process. (More)

CC BY-NC-ND 4.0

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Paper citation in several formats:
Langer, T. and Meisen, T. (2021). Visual Analytics for Industrial Sensor Data Analysis. In Proceedings of the 23rd International Conference on Enterprise Information Systems - Volume 1: ICEIS, ISBN 978-989-758-509-8; ISSN 2184-4992, pages 584-593. DOI: 10.5220/0010399705840593

@conference{iceis21,
author={Tristan Langer. and Tobias Meisen.},
title={Visual Analytics for Industrial Sensor Data Analysis},
booktitle={Proceedings of the 23rd International Conference on Enterprise Information Systems - Volume 1: ICEIS,},
year={2021},
pages={584-593},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010399705840593},
isbn={978-989-758-509-8},
issn={2184-4992},
}

TY - CONF

JO - Proceedings of the 23rd International Conference on Enterprise Information Systems - Volume 1: ICEIS,
TI - Visual Analytics for Industrial Sensor Data Analysis
SN - 978-989-758-509-8
IS - 2184-4992
AU - Langer, T.
AU - Meisen, T.
PY - 2021
SP - 584
EP - 593
DO - 10.5220/0010399705840593