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  4. Generating Stream Processing Applications for IoT-based Process Event Abstraction: A Framework and Architecture
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Generating Stream Processing Applications for IoT-based Process Event Abstraction: A Framework and Architecture

Journal
International Workshop on Enterprise Modeling and Information Systems Architecture (EMISA)
Type
conference contribution
Date Issued
2026-03
Author(s)
Ronny Seiger  
;
Aaron Kurz  
;
Marco Franceschetti  
Abstract
Events play an important role in information systems engineering to facilitate the analysis of relevant happenings in a system or business process via process mining. The Internet of Things (IoT) provides new ways of collecting execution-related events in real world-physical-systems using sensors and actuators. However, these new sources emit data at a too fine-grained level, which prevents process mining from deriving meaningful insights. We present a generic event abstraction framework to lift low-level data to higher level events. Starting with annotated IoT data, we generate stream processing applications that encode change patterns derived from the low-level data. These applications are then used for detecting events and activities at runtime within our proposed software architecture. We evaluate the approach for process executions in smart manufacturing and smart healthcare.
Funding(s)
ProAmbitIon: Online Process Conformance Checking with Ambiguities Driven by the Internet of Things  
Language
English (United States)
Keywords
Internet of Things
Process Mining
Activity detection
Event abstraction
HSG Classification
contribution to scientific community
Refereed
Yes
Publisher
Gesellschaft für Informatik
Pages
2
Event Title
16th International Workshop on Enterprise Modeling and Information Systems Architecture - EMISA 2026
Event Location
Bayreuth, Germany
Event Date
10.-11.03.2026
URL
https://www.alexandria.unisg.ch/handle/20.500.14171/125214
Subject(s)

computer science

Division(s)

ICS - Institute of Co...

File(s)
Thumbnail Image

open.access

Name

2026_EMISA_Activity_Detection_cr.pdf

Size

207.18 KB

Format

Adobe PDF

Checksum (MD5)

ecd2b3fde907a415277e3ce6a0f469b6

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