Abstract
A core component of AI-Augmented Business Process Management Systems (ABPMS) is framing which gives the system process-awareness and defines the boundaries in which the system must operate. When developing an ABPMS, framing can be created manually based on domain knowledge or automatically discovered from prior process executions. Existing process discovery approaches work well for the latter, assuming the individual processes can be identified and analyzed in isolation. However, this is not guaranteed if prior executions were recorded by a system that was not process-aware. In such cases, the bare minimum requirements for process discovery, namely ordered activity sequences grouped by a case identifier, may still be met. For example, treatment activities in a hospital can still be grouped by the patient identifier regardless of how many individual treatment processes (e.g., due to comorbidities) the patient underwent in the same time frame. In this paper, we present a process discovery approach for the above setting. Our focus is on discovering concurrently executed procedural processes, in the presence of shared activities, while the approach itself relies on interpreting declarative process discovery results.
Original language | English |
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Pages (from-to) | 47-58 |
Number of pages | 12 |
Journal | CEUR Workshop Proceedings |
Volume | 3779 |
State | Published - 2024 |
Event | 3rd International Workshop on Process Management in the AI Era, PMAI 2024 - Santiago de Compostela, Spain Duration: 19 Oct 2024 → … |
Bibliographical note
Publisher Copyright:© 2024 Copyright for this paper by its authors.
Keywords
- Declare
- Petri net
- concurrent processes
- multi-model paradigm
- process discovery
- process framing