Publications
2024
Schultheis, Alexander; Malburg, Lukas; Grüger, Joscha; Weich, Justin; Bertrand, Yannis; Bergmann, Ralph; Asensio, Estefanía Serral
Identifying Missing Sensor Values in IoT Time Series Data: A Weight-Based Extension of Similarity Measures for Smart Manufacturing Proceedings Article
In: Case-Based Reasoning Research and Development - 32nd International Conference, ICCBR 2024, Merida, Mexico, July 1-4, 2024, Proceedings, pp. 240–257, Springer., 2024.
Abstract | Links | BibTeX | Tags: {Temporal Case-Based Reasoning, Data Quality Issues}, Time Series Data, Time Series Similarity Measures
@inproceedings{Schultheis.2024_MissingSensorValues,
title = {Identifying Missing Sensor Values in IoT Time Series Data: A Weight-Based Extension of Similarity Measures for Smart Manufacturing},
author = {Alexander Schultheis and Lukas Malburg and Joscha Grüger and Justin Weich and Yannis Bertrand and Ralph Bergmann and Estefanía Serral Asensio},
url = {https://www.wi2.uni-trier.de/shared/publications/2024_ICCBR_SchultheisEtAl.pdf},
doi = {10.1007/978-3-031-63646-2_16},
year = {2024},
date = {2024-01-01},
booktitle = {Case-Based Reasoning Research and Development - 32nd International Conference, ICCBR 2024, Merida, Mexico, July 1-4, 2024, Proceedings},
volume = {14775},
pages = {240–257},
publisher = {Springer.},
series = {Lecture Notes in Computer Science},
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, Data Quality Issues}, Time Series Data, Time Series Similarity Measures},
pubstate = {published},
tppubtype = {inproceedings}
}
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.
Schultheis, Alexander
Exploring a Hybrid Case-Based Reasoning Approach for Time Series Adaptation in Predictive Maintenance Proceedings Article
In: Proceedings of the Workshops at the 32nd International Conference on Case-Based Reasoning (ICCBR-WS 2024) co-located with the 32nd International Conference on Case-Based Reasoning (ICCBR 2024), Merida, Mexico, July 1, 2024, pp. 230–235, CEUR-WS.org., 2024.
Abstract | Links | BibTeX | Tags: {Temporal Case-Based Reasoning, Explainable Case-Based Reasoning, Hybrid Case-Based Reasoning, Internet of Things, Predictive Maintenance}, Time Series Data
@inproceedings{Schultheis.2024,
title = {Exploring a Hybrid Case-Based Reasoning Approach for Time Series Adaptation in Predictive Maintenance},
author = {Alexander Schultheis},
url = {https://www.wi2.uni-trier.de/shared/publications/2024_ICCBR_DC_Schultheis.pdf},
year = {2024},
date = {2024-01-01},
booktitle = {Proceedings of the Workshops at the 32nd International Conference
on Case-Based Reasoning (ICCBR-WS 2024) co-located with the 32nd
International Conference on Case-Based Reasoning (ICCBR 2024), Merida,
Mexico, July 1, 2024},
volume = {3708},
pages = {230–235},
publisher = {CEUR-WS.org.},
series = {CEUR Workshop Proceedings},
abstract = {Predictive Maintenance (PredM) is a vital concept within Industry 4.0, focusing on proactive machine maintenance through analysis of sensor data to uphold quality standards and prevent downtime. PredM traditionally employs data analysis methods or Machine Learning (ML) algorithms for anomaly detection in time series data from sensors. Despite ample error-free data, the occurrence of errors is rare. Case-Based Reasoning (CBR) offers an adaptive artificial intelligence approach effective in domains with limited fault data. The sub-research area of Temporal Case-Based Reasoning (TCBR) explores the processing of time series data based on CBR methods. Integrating TCBR methods into PredM leverages human involvement, addressing data privacy concerns and facilitating knowledge transfer.
While the retrieval in TCBR has already been investigated, the adaptation of the time series contained in the retrieval results has not yet been considered. On this basis, however, it is possible to determine the further course of the time series as an alternative to ML prediction approaches. For the PredM use case with rare fault data, it is important to determine the further course of the time series and how much time remains before a possible fault case occurs. This research summary therefore investigates a hybrid CBR approach that uses deep learning methods like transformers for adaptation. The aim is to predict the course of a time series as accurately as possible, which is evaluated for the PredM use case. Such a hybrid CBR model should also extend an explanatory component for the predicted time series.},
keywords = {{Temporal Case-Based Reasoning, Explainable Case-Based Reasoning, Hybrid Case-Based Reasoning, Internet of Things, Predictive Maintenance}, Time Series Data},
pubstate = {published},
tppubtype = {inproceedings}
}
Predictive Maintenance (PredM) is a vital concept within Industry 4.0, focusing on proactive machine maintenance through analysis of sensor data to uphold quality standards and prevent downtime. PredM traditionally employs data analysis methods or Machine Learning (ML) algorithms for anomaly detection in time series data from sensors. Despite ample error-free data, the occurrence of errors is rare. Case-Based Reasoning (CBR) offers an adaptive artificial intelligence approach effective in domains with limited fault data. The sub-research area of Temporal Case-Based Reasoning (TCBR) explores the processing of time series data based on CBR methods. Integrating TCBR methods into PredM leverages human involvement, addressing data privacy concerns and facilitating knowledge transfer.
While the retrieval in TCBR has already been investigated, the adaptation of the time series contained in the retrieval results has not yet been considered. On this basis, however, it is possible to determine the further course of the time series as an alternative to ML prediction approaches. For the PredM use case with rare fault data, it is important to determine the further course of the time series and how much time remains before a possible fault case occurs. This research summary therefore investigates a hybrid CBR approach that uses deep learning methods like transformers for adaptation. The aim is to predict the course of a time series as accurately as possible, which is evaluated for the PredM use case. Such a hybrid CBR model should also extend an explanatory component for the predicted time series.
While the retrieval in TCBR has already been investigated, the adaptation of the time series contained in the retrieval results has not yet been considered. On this basis, however, it is possible to determine the further course of the time series as an alternative to ML prediction approaches. For the PredM use case with rare fault data, it is important to determine the further course of the time series and how much time remains before a possible fault case occurs. This research summary therefore investigates a hybrid CBR approach that uses deep learning methods like transformers for adaptation. The aim is to predict the course of a time series as accurately as possible, which is evaluated for the PredM use case. Such a hybrid CBR model should also extend an explanatory component for the predicted time series.
2023
Malburg, Lukas; Schultheis, Alexander; Bergmann, Ralph
Modeling and Using Complex IoT Time Series Data in Case-Based Reasoning: From Application Scenarios to Implementations Proceedings Article
In: Malburg, Lukas; Verma, Deepika (Ed.): Proceedings of the Workshops at the 31st International Conference on Case-Based Reasoning (ICCBR-WS 2023) co-located with the 31st International Conference on Case-Based Reasoning (ICCBR 2023), Aberdeen, Scotland, UK, July 17, 2023, pp. 81–96, CEUR-WS.org, 2023.
Abstract | Links | BibTeX | Tags: {Case-Based Reasoning, Internet of Things, ProCAKE}, Temporal Case-Based Reasoning, Time Series Data
@inproceedings{Malburg.2023_TimeSeriesInCBR,
title = {Modeling and Using Complex IoT Time Series Data in Case-Based Reasoning:
From Application Scenarios to Implementations},
author = {Lukas Malburg and Alexander Schultheis and Ralph Bergmann},
editor = {Lukas Malburg and Deepika Verma},
url = {http://www.wi2.uni-trier.de/shared/publications/2023_Malburg_TimeSeriesInCBR.pdf},
year = {2023},
date = {2023-01-01},
booktitle = {Proceedings of the Workshops at the 31st International Conference
on Case-Based Reasoning (ICCBR-WS 2023) co-located with the 31st
International Conference on Case-Based Reasoning (ICCBR 2023), Aberdeen,
Scotland, UK, July 17, 2023},
volume = {3438},
pages = {81–96},
publisher = {CEUR-WS.org},
series = {CEUR Workshop Proceedings},
abstract = {The research area of Internet of Things (IoT) is gaining more relevance for several domains and application areas, including Case-Based Reasoning (CBR). However, IoT data is characterized by high volumes and variance of data types, making the application of CBR methods difficult. Since only few works have been published in this area so far, the integration and consideration of complex IoT data such as time series data in CBR frameworks is still in its infancy. To catch up with the current state-of-the-art, we present a comprehensive literature review on Temporal Case-Based Reasoning and time series data in CBR as part of our contribution. Furthermore, we present typical application scenarios for using IoT time series data in practice that can be addressed in further research. To build suitable CBR implementations for that purpose, we define a procedure model that can be used for time series data in CBR. In this context, we address the implementation of the application scenarios in the ProCAKE CBR framework.},
keywords = {{Case-Based Reasoning, Internet of Things, ProCAKE}, Temporal Case-Based Reasoning, Time Series Data},
pubstate = {published},
tppubtype = {inproceedings}
}
The research area of Internet of Things (IoT) is gaining more relevance for several domains and application areas, including Case-Based Reasoning (CBR). However, IoT data is characterized by high volumes and variance of data types, making the application of CBR methods difficult. Since only few works have been published in this area so far, the integration and consideration of complex IoT data such as time series data in CBR frameworks is still in its infancy. To catch up with the current state-of-the-art, we present a comprehensive literature review on Temporal Case-Based Reasoning and time series data in CBR as part of our contribution. Furthermore, we present typical application scenarios for using IoT time series data in practice that can be addressed in further research. To build suitable CBR implementations for that purpose, we define a procedure model that can be used for time series data in CBR. In this context, we address the implementation of the application scenarios in the ProCAKE CBR framework.