Identifying Missing Sensor Values in IoT Time Series Data: A Weight-Based Extension of Similarity Measures for Smart Manufacturing. Schultheis, A., Malburg, L., Grüger, J., Weich, J., Bertrand, Y., Bergmann, R., & Serral Asensio, E. In Case-Based Reasoning Research and Development - 32nd International Conference, ICCBR 2024, Merida, Mexico, July 1-4, 2024, Proceedings, volume 14775, of Lecture Notes in Computer Science, pages 240–257, 2024. Springer..
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Paper doi abstract bibtex 5 downloads
Paper doi abstract bibtex 5 downloads Smart Manufacturing integrates methods of Artificial Intelligence and the Internet of Things into processes to enhance efficiency and flexibility. However, analysis of time series sensor data, crucial for process optimization, is susceptible to Data Quality Issues (DQIs) and can lead to operational problems. Traditional machine learning approaches struggle with limited error data availability in addressing DQIs. The knowledge-driven approach of Case-Based Reasoning targets this issue by reusing experiences regarding already identified DQIs. While some DQIs can be detected using conventional similarity measures, the common, frequently occurring DQI type of Missing Sensor Values pose challenges that cannot be solved using established measures. To address this, this paper proposes a weight-based extension of similarity measures for time series data. This extension aims at the identification and handling of missing sensor values in smart manufacturing processes. Furthermore, analog extensions of established time series measures are presented and possible areas of application outside the DQI domain are outlined.
@inproceedings{Schultheis.2024_MissingSensorValues,
author = {Schultheis, Alexander and Malburg, Lukas and Grüger, Joscha and Weich, Justin and Bertrand, Yannis and Bergmann, Ralph and Serral Asensio, Estefanía},
title = {{Identifying Missing Sensor Values in IoT Time Series Data: A Weight-Based Extension of Similarity Measures for Smart Manufacturing}},
booktitle = {Case-Based Reasoning Research and Development - 32nd International Conference, {ICCBR} 2024, Merida, Mexico, July 1-4, 2024, Proceedings},
series = {Lecture Notes in Computer Science},
pages = {240--257},
volume = {14775},
publisher = {Springer.},
year = {2024},
doi = {10.1007/978-3-031-63646-2\_16},
abstract = {{Smart Manufacturing integrates methods of Artificial Intelligence and the Internet of Things into processes to enhance efficiency and flexibility. However, analysis of time series sensor data, crucial for process optimization, is susceptible to Data Quality Issues (DQIs) and can lead to operational problems. Traditional machine learning approaches struggle with limited error data availability in addressing DQIs. The knowledge-driven approach of Case-Based Reasoning targets this issue by reusing experiences regarding already identified DQIs. While some DQIs can be detected using conventional similarity measures, the common, frequently occurring DQI type of Missing Sensor Values pose challenges that cannot be solved using established measures. To address this, this paper proposes a weight-based extension of similarity measures for time series data. This extension aims at the identification and handling of missing sensor values in smart manufacturing processes. Furthermore, analog extensions of established time series measures are presented and possible areas of application outside the DQI domain are outlined.}},
keywords = {{Temporal Case-Based Reasoning, Time Series Data, Time Series Similarity Measures, Data Quality Issues}},
url = {https://www.wi2.uni-trier.de/shared/publications/2024_ICCBR_SchultheisEtAl.pdf}
}Downloads: 5
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