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QTab: Inter-case-aware remaining time prediction via queuing networks and tabular machine learning
Amiri Elyasi, Keyvan
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Tschalzev, Andrej
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Aa, Han van der
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Stuckenschmidt, Heiner
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Document Type:
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Conference or workshop publication
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Year of publication:
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2027
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Book title:
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Enterprise design, operations, and computing : 30th International Conference, EDOC 2026, Enschede, The Netherlands, September 15–18, 2026, Proceedings
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The title of a journal, publication series:
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Lecture Notes in Computer Science
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Volume:
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17189
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Page range:
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tba
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Conference title:
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30th International Conference on Enterprise Design, Operations, and Computing (EDOC 2026)
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Location of the conference venue:
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Enschede, The Netherlands
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Date of the conference:
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15.-18.09.2026
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Publisher:
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Gill, Asif
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Di Francescomarino, Chiara
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Guizzardi, Renata
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Sales, Tiago Prince
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Place of publication:
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Berlin [u.a.]
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Publishing house:
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Springer
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ISBN:
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978-3-032-42899-8
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ISSN:
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0302-9743 , 1611-3349
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Related URLs:
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Publication language:
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English
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Institution:
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School of Business Informatics and Mathematics > Practical Computer Science II: Artificial Intelligence (Stuckenschmidt 2009-)
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Subject:
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004 Computer science, internet
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Keywords (English):
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predictive process monitoring , remaining time prediction , queuing networks , machine learning for tabular data
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Abstract:
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Process-aware information systems support the execution and monitoring of business processes. The recorded event data can be leveraged to predict the remaining time of running cases, enabling organizations to meet service-level targets, and enhance operational performance.
However, most existing approaches treat cases as being independent from each other, even though there often is contention between cases for shared resources. While some approaches account for inter-case dependencies, they overlook the queuing dynamics arising from work handovers between resources. Moreover, inter-case-aware approaches often use traditional tree-based models, which generally achieve lower accuracy than modern deep learning models.
To overcome these limitations, we introduce QTab, a novel remaining time prediction approach powered by two components: (1) modeling of resource interactions and work handovers through a queuing network to derive informative inter-case features; (2) Modern tabular neural networks trained in sophisticated ensembling pipelines. Experimental results on eight real-world event logs show that QTab achieves competitive or superior predictive accuracy compared to existing tree-based and deep learning approaches in remaining time prediction. At the same time, it offers better scalability in feature extraction and improved efficiency in model training.
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 Search Authors in
BASE:
Amiri Elyasi, Keyvan
;
Tschalzev, Andrej
;
Aa, Han van der
;
Stuckenschmidt, Heiner
Google Scholar:
Amiri Elyasi, Keyvan
;
Tschalzev, Andrej
;
Aa, Han van der
;
Stuckenschmidt, Heiner
ORCID:
Amiri Elyasi, Keyvan ; Tschalzev, Andrej  ORCID: 0000-0002-0638-5744 ; Aa, Han van der ORCID: 0000-0002-4200-4937 ; Stuckenschmidt, Heiner ORCID: 0000-0002-0209-3859
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