DIAG Approach: Introducing the Cognitive Process Mining by an Ontology-Driven Approach to Diagnose and Explain Concept Drifts Article Swipe
Sina Namaki Araghi
,
Franck Fontanili
,
Arkopaul Sarkar
,
Elyes Lamine
,
Mohamed Hedi Karray
,
Frédérick Bénaben
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/modelling5010006
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/modelling5010006
The remarkable growth of process mining applications in care pathway monitoring is undeniable. One of the sub-emerging case studies is the use of patients’ location data in process mining analyses. While the streamlining of published works is focused on introducing process discovery algorithms, there is a necessity to address challenges beyond that. Literature analysis indicates that explainability, reasoning, and characterizing the root causes of process drifts in healthcare processes constitute an important but overlooked challenge. In addition, incorporating domain-specific knowledge into process discovery could be a significant contribution to process mining literature. Therefore, we mitigate the issue by introducing cognitive process mining through the DIAG approach, which consists of a meta-model and an algorithm. This approach enables reasoning and diagnosing in process mining through an ontology-driven framework. With DIAG, we modeled the healthcare semantics in a process mining application and diagnosed the causes of drifts in patients’ pathways. We performed an experiment in a hospital living lab to examine the effectiveness of our approach.
Related Topics
Concepts
Process mining
Ontology
Process (computing)
Computer science
Data science
Domain (mathematical analysis)
Cognition
Semantics (computer science)
Business process discovery
Abstract process
Data mining
Work in process
Business process management
Business process modeling
Engineering
Business process
Medicine
Philosophy
Programming language
Operations management
Psychiatry
Mathematical analysis
Epistemology
Operating system
Mathematics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/modelling5010006
- https://www.mdpi.com/2673-3951/5/1/6/pdf?version=1703669609
- OA Status
- gold
- Cited By
- 5
- References
- 28
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4390274804
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4390274804Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/modelling5010006Digital Object Identifier
- Title
-
DIAG Approach: Introducing the Cognitive Process Mining by an Ontology-Driven Approach to Diagnose and Explain Concept DriftsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-12-27Full publication date if available
- Authors
-
Sina Namaki Araghi, Franck Fontanili, Arkopaul Sarkar, Elyes Lamine, Mohamed Hedi Karray, Frédérick BénabenList of authors in order
- Landing page
-
https://doi.org/10.3390/modelling5010006Publisher landing page
- PDF URL
-
https://www.mdpi.com/2673-3951/5/1/6/pdf?version=1703669609Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.mdpi.com/2673-3951/5/1/6/pdf?version=1703669609Direct OA link when available
- Concepts
-
Process mining, Ontology, Process (computing), Computer science, Data science, Domain (mathematical analysis), Cognition, Semantics (computer science), Business process discovery, Abstract process, Data mining, Work in process, Business process management, Business process modeling, Engineering, Business process, Medicine, Philosophy, Programming language, Operations management, Psychiatry, Mathematical analysis, Epistemology, Operating system, MathematicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
5Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 3, 2024: 2Per-year citation counts (last 5 years)
- References (count)
-
28Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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