A window of opportunity: Active window tracking for mining work practices
I Beerepoot, D Barenholz, S Beekhuis… - … on Process Mining …, 2023 - ieeexplore.ieee.org
The field of process mining has evolved from discovering single work processes towards
providing broad insights into peoples' work practices. Existing techniques can be used to …
providing broad insights into peoples' work practices. Existing techniques can be used to …
[HTML][HTML] Business process variant analysis: Survey and classification
It is common for business processes to exhibit a high degree of internal heterogeneity, in the
sense that the executions of the process differ widely from each other due to contextual …
sense that the executions of the process differ widely from each other due to contextual …
Prescriptive process monitoring for cost-aware cycle time reduction
Reducing cycle time is a recurrent concern in the field of business process management.
Depending on the process, various interventions may be triggered to reduce the cycle time …
Depending on the process, various interventions may be triggered to reduce the cycle time …
Personalization in practice: Methods and applications
D Goldenberg, K Kofman, J Albert, S Mizrachi… - Proceedings of the 14th …, 2021 - dl.acm.org
Personalization is one of the key applications in machine learning with widespread usage
across e-commerce, entertainment, production, healthcare and many other industries. While …
across e-commerce, entertainment, production, healthcare and many other industries. While …
How well can large language models explain business processes?
D Fahland, F Fournier, L Limonad, I Skarbovsky… - arXiv preprint arXiv …, 2024 - arxiv.org
Large Language Models (LLMs) are likely to play a prominent role in future AI-augmented
business process management systems (ABPMSs) catering functionalities across all system …
business process management systems (ABPMSs) catering functionalities across all system …
[HTML][HTML] Machine learning in business process management: A systematic literature review
Abstract Machine learning (ML) provides algorithms to create computer programs based on
data without explicitly programming them. In business process management (BPM), ML …
data without explicitly programming them. In business process management (BPM), ML …
Prescriptive process monitoring based on causal effect estimation
Prescriptive process monitoring methods seek to control the execution of a business process
by triggering interventions, at runtime, to optimise one or more performance measure (s) …
by triggering interventions, at runtime, to optimise one or more performance measure (s) …
Data-driven dynamic causality analysis of industrial systems using interpretable machine learning and process mining
The complexity of industrial processes imposes a lot of challenges in building accurate and
representative causal models for abnormal events diagnosis, control and maintenance of …
representative causal models for abnormal events diagnosis, control and maintenance of …
A framework for explainable concept drift detection in process mining
JN Adams, SJ van Zelst, L Quack, K Hausmann… - … Conference, BPM 2021 …, 2021 - Springer
Rapidly changing business environments expose companies to high levels of uncertainty.
This uncertainty manifests itself in significant changes that tend to occur over the lifetime of a …
This uncertainty manifests itself in significant changes that tend to occur over the lifetime of a …
Prescriptive process monitoring under resource constraints: a causal inference approach
Prescriptive process monitoring is a family of techniques to optimize the performance of a
business process by triggering interventions at runtime. Existing prescriptive process …
business process by triggering interventions at runtime. Existing prescriptive process …