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.