Toward IoT-based Process Analytics: Extending Event Knowledge Graphs with Ambiguity
Journal
Enterprise, Business-Process and Information Systems Modeling
Type
conference contribution
Date Issued
2025-06
Author(s)
Abstract
Digital traces of processes executed in the physical world without Information Systems (IS) support are composed of high-level events derived from low-level physical world events (e.g., from IoT). Due to this, frequently these high-level events are ambiguous, i.e., they yield multiple interpretations. As current IS lack ambiguity-awareness, ambiguity in digital traces can compromise process analytics. Motivated by this and current trends toward multi-dimensional analytics, we introduce an ambiguity-aware object-centric representation of digital traces by extending Event Knowledge Graphs with ambiguity. We integrate its construction into a framework to enable analytics for ambiguous IoT-based digital traces. A prototype implementation shows the applicability of the construction from ambiguous process event streams in online settings.
Language
English
Keywords
event knowledge graphs
ambiguity
iot-based analytics
HSG Classification
contribution to scientific community
Refereed
Yes
Book title
Lecture Notes in Business Information Processing
Publisher
Springer
Event Title
Business Process Modeling, Development, and Support (BPMDS) Working Conference
Event Location
Vienna, Austria
Event Date
16-17 June, 2025
Subject(s)
Division(s)
File(s)![Thumbnail Image]()
open.access
Name
BPMDS2025___Extending_Event_Knowledge_Graphs_with_Ambiguity.pdf
Size
810.96 KB
Format
Adobe PDF
Checksum (MD5)
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