Integration of Time Series Embedding for Efficient Retrieval in Case-Based Reasoning. Weich, J., Schultheis, A., Hoffmann, M., & Bergmann, R. In Case-Based Reasoning Research and Development - 33rd International Conference, ICCBR 2025, Biarritz, France, June 30 - July 3rd, 2025, Proceedings, of Lecture Notes in Computer Science, 2025. Springer.. Accepted for Publication.abstract bibtex 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.
@inproceedings{WeichSHB2025,
author = {Justin Weich and Alexander Schultheis and Maximilian Hoffmann and Ralph Bergmann},
title = {{Integration of Time Series Embedding for Efficient Retrieval in Case-Based Reasoning}},
booktitle = {Case-Based Reasoning Research and Development - 33rd International Conference, {ICCBR} 2025, Biarritz, France, June 30 - July 3rd, 2025, Proceedings},
series = {Lecture Notes in Computer Science},
publisher = {Springer.},
year = {2025},
note = {{Accepted for Publication.}},
keywords = {Temporal Case-Based Reasoning, Time Series Data, Time Series Embedding, Time Series Similarity Measure, Siamese Neural Networks},
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.},
}
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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.","bibtex":"@inproceedings{WeichSHB2025,\n author = {Justin Weich and Alexander Schultheis and Maximilian Hoffmann and Ralph Bergmann},\n title = {{Integration of Time Series Embedding for Efficient Retrieval in Case-Based Reasoning}},\n booktitle = {Case-Based Reasoning Research and Development - 33rd International Conference, {ICCBR} 2025, Biarritz, France, June 30 - July 3rd, 2025, Proceedings},\n\tseries = {Lecture Notes in Computer Science},\n publisher = {Springer.},\n year = {2025},\n note \t\t = {{Accepted for Publication.}},\n\tkeywords \t = {Temporal Case-Based Reasoning, Time Series Data, Time Series Embedding, Time Series Similarity Measure, Siamese Neural Networks},\n\tabstract \t = {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.},\n}\n\n","author_short":["Weich, J.","Schultheis, A.","Hoffmann, M.","Bergmann, R."],"key":"WeichSHB2025","id":"WeichSHB2025","bibbaseid":"weich-schultheis-hoffmann-bergmann-integrationoftimeseriesembeddingforefficientretrievalincasebasedreasoning-2025","role":"author","urls":{},"keyword":["Temporal Case-Based Reasoning","Time Series Data","Time Series Embedding","Time Series Similarity Measure","Siamese Neural Networks"],"metadata":{"authorlinks":{}}},"bibtype":"inproceedings","biburl":"https://web.wi2.uni-trier.de/publications/WI2Publikationen_IoT.bib","dataSources":["MSp3DzP4ToPojqkFy","Td7BJ334QwxWK4vLW"],"keywords":["temporal case-based reasoning","time series data","time series embedding","time series similarity measure","siamese neural networks"],"search_terms":["integration","time","series","embedding","efficient","retrieval","case","based","reasoning","weich","schultheis","hoffmann","bergmann"],"title":"Integration of Time Series Embedding for Efficient Retrieval in Case-Based Reasoning","year":2025}