A semi-automated approach to detecting process-level activities from sensor data
Journal
Procedia Computer Science
ISSN
1877-0509
Type
conference contribution
Date Issued
2025-04
Author(s)
García-Bañuelos, Luciano
;
González González, Mauricio Jacobo
;
;
;
Silva Trujillo, Alejandra Guadalupe
Abstract
Internet of Things (IoT) technologies can be leveraged to monitor and check the conformance of business process executions in the absence of a Business Process Management (BPM) System. This poses the challenge of detecting process activities from lowlevel sensor data by means of event abstraction. Existing methods rely on fully supervised approaches, combining sensor data and process event logs to train classification models. However, frequently these logs are not available, invalidating the applicability of such methods. In this paper, we propose a semi-automated approach to detecting process activities from sensor data based on frequently repeated subsequences and automata. We evaluate the approach with a proof-of-concept implementation and data from a small-scale smart factory. Our evaluation demonstrates the applicability of the proposed approach and its effectiveness in detecting process activities from sensor data to support domain experts with data analysis through automated suggestions.
Language
English (United States)
Keywords
Internet of Things
Business Process Management
Activity detection
Cyber-physical Systems
Sensors
Industry 4.0
HSG Classification
contribution to scientific community
Refereed
Yes
Publisher
Elsevier
Pages
8
Event Title
The 8th International Conference on Emerging Data and Industry (EDI40)
Event Location
Patras, Greece
Event Date
April 22-24, 2025
Subject(s)
Division(s)
File(s)![Thumbnail Image]()
Name
EDI_2025___Detecting_Activities_from_Sensor_Data.pdf
Size
1.62 MB
Format
Adobe PDF
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