Discriminative Rule Learning for Outcome-Guided Process Model Discovery

• URL: http://arxiv.org/abs/2510.27343v1
• Date: Fri, 31 Oct 2025 10:25:19 GMT
• Title: Discriminative Rule Learning for Outcome-Guided Process Model Discovery
• Authors: Ali Norouzifar, Wil van der Aalst,
• Abstract summary: Event logs offer a rich foundation for understanding and improving business processes.<n>It is possible to distinguish between desirable and undesirable process executions.<n>This distinction presents an opportunity to guide process discovery in a more outcome-aware manner.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Event logs extracted from information systems offer a rich foundation for understanding and improving business processes. In many real-world applications, it is possible to distinguish between desirable and undesirable process executions, where desirable traces reflect efficient or compliant behavior, and undesirable ones may involve inefficiencies, rule violations, delays, or resource waste. This distinction presents an opportunity to guide process discovery in a more outcome-aware manner. Discovering a single process model without considering outcomes can yield representations poorly suited for conformance checking and performance analysis, as they fail to capture critical behavioral differences. Moreover, prioritizing one behavior over the other may obscure structural distinctions vital for understanding process outcomes. By learning interpretable discriminative rules over control-flow features, we group traces with similar desirability profiles and apply process discovery separately within each group. This results in focused and interpretable models that reveal the drivers of both desirable and undesirable executions. The approach is implemented as a publicly available tool and it is evaluated on multiple real-life event logs, demonstrating its effectiveness in isolating and visualizing critical process patterns.

Related papers

• Procedural Mistake Detection via Action Effect Modeling [10.358293338390716]
  Action Effect Modeling (AEM) is a unified framework that captures action execution and its outcomes through a probabilistic formulation.<n>AEM identifies the outcome of an action by selecting the most informative effect frame based on semantic relevance and visual quality.<n>It then extracts complementary cues from visual grounding and symbolic scene graphs, aligning them in a shared latent space to form robust effect-aware representations.
  arXiv  Detail & Related papers  (2025-12-03T05:56:17Z)
• Bounding Causal Effects and Counterfactuals [0.0]
  This thesis addresses challenges by systematically comparing bounding algorithms across multiple causal scenarios.<n>We implement, extend, and unify state-of-the-art methods within a common evaluation framework.<n>Our empirical study spans thousands of randomized simulations involving both discrete and continuous data-generating processes.
  arXiv  Detail & Related papers  (2025-08-19T08:13:34Z)
• Offline Action-Free Learning of Ex-BMDPs by Comparing Diverse Datasets [87.62730694973696]
  This paper introduces CRAFT, a sample-efficient algorithm leveraging differences in controllable feature dynamics across agents to learn representations.<n>We provide theoretical guarantees for CRAFT's performance and demonstrate its feasibility on a toy example.
  arXiv  Detail & Related papers  (2025-03-26T22:05:57Z)
• Do We Need to Verify Step by Step? Rethinking Process Supervision from a Theoretical Perspective [59.61868506896214]
  We show that under standard data coverage assumptions, reinforcement learning is no more statistically difficult than through process supervision.<n>We prove that any policy's advantage function can serve as an optimal process reward model.
  arXiv  Detail & Related papers  (2025-02-14T22:21:56Z)
• Pattern based learning and optimisation through pricing for bin packing problem [50.83768979636913]
  We argue that when problem conditions such as the distributions of random variables change, the patterns that performed well in previous circumstances may become less effective. We propose a novel scheme to efficiently identify patterns and dynamically quantify their values for each specific condition. Our method quantifies the value of patterns based on their ability to satisfy constraints and their effects on the objective value.
  arXiv  Detail & Related papers  (2024-08-27T17:03:48Z)
• Explanatory Model Monitoring to Understand the Effects of Feature Shifts on Performance [61.06245197347139]
  We propose a novel approach to explain the behavior of a black-box model under feature shifts. We refer to our method that combines concepts from Optimal Transport and Shapley Values as Explanatory Performance Estimation.
  arXiv  Detail & Related papers  (2024-08-24T18:28:19Z)
• Mining a Minimal Set of Behavioral Patterns using Incremental Evaluation [3.16536213610547]
  Existing approaches to behavioral pattern mining suffer from two limitations. First, they show limited scalability as incremental computation is incorporated only in the generation of pattern candidates. Second, process analysis based on mined patterns shows limited effectiveness due to an overwhelmingly large number of patterns obtained in practical application scenarios.
  arXiv  Detail & Related papers  (2024-02-05T11:41:37Z)
• Impossibility Theorems for Feature Attribution [21.88229793890961]
  We show that for moderately rich model classes, any feature attribution method can provably fail to improve on random guessing for inferring model behaviour. Our results apply to common end-tasks such as characterizing local model behaviour, identifying spurious features, and algorithmic recourse.
  arXiv  Detail & Related papers  (2022-12-22T17:03:57Z)
• Chain of Thought Imitation with Procedure Cloning [129.62135987416164]
  We propose procedure cloning, which applies supervised sequence prediction to imitate the series of expert computations. We show that imitating the intermediate computations of an expert's behavior enables procedure cloning to learn policies exhibiting significant generalization to unseen environment configurations.
  arXiv  Detail & Related papers  (2022-05-22T13:14:09Z)
• Exploring Business Process Deviance with Sequential and Declarative Patterns [3.039637436705478]
  Deviance mining is concerned with uncovering the reasons for deviant executions by analyzing event logs stored by the systems supporting the execution of a business process. The problem of explaining deviations in business processes is first investigated by using features based on sequential and declarative patterns. The explanations are then extracted by direct and indirect methods for rule induction.
  arXiv  Detail & Related papers  (2021-11-24T12:16:07Z)
• Process discovery on deviant traces and other stranger things [6.974048370610024]
  We focus on declarative processes and embrace the less-popular view of process discovery as a binary supervised learning task. We deepen how the valuable information brought by both these two sets can be extracted and formalised into a model that is "optimal" according to user-defined goals.
  arXiv  Detail & Related papers  (2021-09-30T06:58:34Z)
• Hierarchical Variational Imitation Learning of Control Programs [131.7671843857375]
  We propose a variational inference method for imitation learning of a control policy represented by parametrized hierarchical procedures (PHP) Our method discovers the hierarchical structure in a dataset of observation-action traces of teacher demonstrations, by learning an approximate posterior distribution over the latent sequence of procedure calls and terminations. We demonstrate a novel benefit of variational inference in the context of hierarchical imitation learning: in decomposing the policy into simpler procedures, inference can leverage acausal information that is unused by other methods.
  arXiv  Detail & Related papers  (2019-12-29T08:57:02Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.