Case-Based Activity Detection from Segmented Internet of Things Data
Journal
Case-Based Reasoning Research and Development
Series
LNCS
Type
conference contribution
Date Issued
2025-06
Author(s)
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 prototypeas 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.
Language
English (United States)
Keywords
Temporal Case-Based Reasoning
Time Series Data
Activity Detection
Internet of Things
HSG Classification
contribution to scientific community
Refereed
Yes
Publisher
Springer
Volume
15662
Pages
15
Event Title
33rd International Conference on Case-Based Reasoning (ICCBR)
Event Location
Biarritz, France
Event Date
June 30 - July 3rd, 2025
Subject(s)
Division(s)
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Name
2025_ICCBR__Case_Based_Activity_Detection_from_Segmented_Internet_of_Things_Data.pdf
Size
800.8 KB
Format
Adobe PDF
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