Publications
2025
Seiger, Ronny; Schultheis, Alexander; Bergmann, Ralph
Case-Based Activity Detection from Segmented Internet of Things Data Proceedings Article
In: Case-Based Reasoning Research and Development - 33rd International Conference, ICCBR 2025, Biarritz, France, June 30 - July 3rd, 2025, Proceedings, Springer., 2025, (Accepted for Publication.).
Abstract | BibTeX | Tags: Activity Detection, Internet of Things, Temporal Case-Based Reasoning, Time Series Data
@inproceedings{SeigerSB2025,
title = {Case-Based Activity Detection from Segmented Internet of Things Data},
author = {Ronny Seiger and Alexander Schultheis and Ralph Bergmann},
year = {2025},
date = {2025-01-01},
booktitle = {Case-Based Reasoning Research and Development - 33rd International Conference, ICCBR 2025, Biarritz, France, June 30 - July 3rd, 2025, Proceedings},
publisher = {Springer.},
series = {Lecture Notes in Computer Science},
abstract = {The use of Internet of Things (IoT) technologies drives the automation of business processes. However, such environments often lack process awareness and corresponding systems to monitor process executions. Due to its too fine-grained nature and variations, the direct use of data from IoT devices for monitoring is problematic, requiring an event abstraction step to lift the data to the business process level. This work investigates the application of Temporal Case-Based Reasoning (TCBR) as a novel experience-based approach to detect process activity executions in IoT data. The proposed TCBR approach uses activity signatures - representations of process and IoT data for an activity prototype - as a case base to classify unknown IoT time series data from a smart factory. A data flow architecture is presented that supports analysts in selecting a suitable activity prototype and evaluating its quality for activity detection. The results enable both, the development of high-quality activity detection services and the identification of improvement opportunities in IoT monitoring systems. The approach is evaluated using data produced by a smart factory. The results indicate that the TCBR methods used are very suitable for detecting activities in this IoT use case.},
note = {Accepted for Publication.},
keywords = {Activity Detection, Internet of Things, Temporal Case-Based Reasoning, Time Series Data},
pubstate = {published},
tppubtype = {inproceedings}
}
The use of Internet of Things (IoT) technologies drives the automation of business processes. However, such environments often lack process awareness and corresponding systems to monitor process executions. Due to its too fine-grained nature and variations, the direct use of data from IoT devices for monitoring is problematic, requiring an event abstraction step to lift the data to the business process level. This work investigates the application of Temporal Case-Based Reasoning (TCBR) as a novel experience-based approach to detect process activity executions in IoT data. The proposed TCBR approach uses activity signatures - representations of process and IoT data for an activity prototype - as a case base to classify unknown IoT time series data from a smart factory. A data flow architecture is presented that supports analysts in selecting a suitable activity prototype and evaluating its quality for activity detection. The results enable both, the development of high-quality activity detection services and the identification of improvement opportunities in IoT monitoring systems. The approach is evaluated using data produced by a smart factory. The results indicate that the TCBR methods used are very suitable for detecting activities in this IoT use case.
Weich, Justin; Schultheis, Alexander; Hoffmann, Maximilian; Bergmann, Ralph
Integration of Time Series Embedding for Efficient Retrieval in Case-Based Reasoning Proceedings Article
In: Case-Based Reasoning Research and Development - 33rd International Conference, ICCBR 2025, Biarritz, France, June 30 - July 3rd, 2025, Proceedings, Springer., 2025, (Accepted for Publication.).
Abstract | BibTeX | Tags: Siamese Neural Networks, Temporal Case-Based Reasoning, Time Series Data, Time Series Embedding, Time Series Similarity Measure
@inproceedings{WeichSHB2025,
title = {Integration of Time Series Embedding for Efficient Retrieval in Case-Based Reasoning},
author = {Justin Weich and Alexander Schultheis and Maximilian Hoffmann and Ralph Bergmann},
year = {2025},
date = {2025-01-01},
booktitle = {Case-Based Reasoning Research and Development - 33rd International Conference, ICCBR 2025, Biarritz, France, June 30 - July 3rd, 2025, Proceedings},
publisher = {Springer.},
series = {Lecture Notes in Computer Science},
abstract = {The increasing volume of time series data in Industry 4.0 applications creates substantial challenges for real-time data analysis. Such analyses that are conducted in the research area of Temporal Case-Based Reasoning (TCBR) face performance problems due to complex similarity measures. One potential approach already proven in other domains for addressing these problems is the usage of embedding techniques for time series data, which map these data into a simplified vector representation. Therefore, this paper investigates the integration of time series embedding techniques in the context of Case-Based Reasoning (CBR) to improve retrieval efficiency. Therefore, requirements for the application of embedding techniques in CBR are derived. A systematic literature study identifies possible approaches that are analyzed based on the requirements, with the result that no approach is suitable for the application. Therefore, a novel embedding architecture is proposed, using a Siamese neural network approach that can be trained with similarity values. The architecture is prototypically implemented in the ProCAKE framework and evaluated in an Internet of Things use case from a smart factory. The results demonstrate that the embedding-based retrieval achieves classification performance comparable to traditional similarity measures while significantly reducing retrieval time.},
note = {Accepted for Publication.},
keywords = {Siamese Neural Networks, Temporal Case-Based Reasoning, Time Series Data, Time Series Embedding, Time Series Similarity Measure},
pubstate = {published},
tppubtype = {inproceedings}
}
The increasing volume of time series data in Industry 4.0 applications creates substantial challenges for real-time data analysis. Such analyses that are conducted in the research area of Temporal Case-Based Reasoning (TCBR) face performance problems due to complex similarity measures. One potential approach already proven in other domains for addressing these problems is the usage of embedding techniques for time series data, which map these data into a simplified vector representation. Therefore, this paper investigates the integration of time series embedding techniques in the context of Case-Based Reasoning (CBR) to improve retrieval efficiency. Therefore, requirements for the application of embedding techniques in CBR are derived. A systematic literature study identifies possible approaches that are analyzed based on the requirements, with the result that no approach is suitable for the application. Therefore, a novel embedding architecture is proposed, using a Siamese neural network approach that can be trained with similarity values. The architecture is prototypically implemented in the ProCAKE framework and evaluated in an Internet of Things use case from a smart factory. The results demonstrate that the embedding-based retrieval achieves classification performance comparable to traditional similarity measures while significantly reducing retrieval time.
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.