Prescriptive Process Monitoring: Quo Vadis?

• URL: http://arxiv.org/abs/2112.01769v1
• Date: Fri, 3 Dec 2021 08:06:24 GMT
• Title: Prescriptive Process Monitoring: Quo Vadis?
• Authors: Kateryna Kubrak, Fredrik Milani, Alexander Nolte, Marlon Dumas
• Abstract summary: The paper studies existing methods in this field via a Systematic Literature Review ( SLR) The SLR provides insights into challenges and areas for future research that could enhance the usefulness and applicability of prescriptive process monitoring methods.
• Score: 64.39761523935613
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Prescriptive process monitoring methods seek to optimize a business process by recommending interventions at runtime to prevent negative outcomes or poorly performing cases. In recent years, various prescriptive process monitoring methods have been proposed. This paper studies existing methods in this field via a Systematic Literature Review (SLR). In order to structure the field, the paper proposes a framework for characterizing prescriptive process monitoring methods according to their performance objective, performance metrics, intervention types, modeling techniques, data inputs, and intervention policies. The SLR provides insights into challenges and areas for future research that could enhance the usefulness and applicability of prescriptive process monitoring methods. The paper highlights the need to validate existing and new methods in real-world settings, to extend the types of interventions beyond those related to the temporal and cost perspectives, and to design policies that take into account causality and second-order effects.

Related papers

• 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)
• Process Variant Analysis Across Continuous Features: A Novel Framework [0.0]
  This research addresses the challenge of effectively segmenting cases within operational processes. We present a novel approach employing a sliding window technique combined with the earth mover's distance to detect changes in control flow behavior. We validate our methodology through a real-life case study in collaboration with UWV, the Dutch employee insurance agency.
  arXiv  Detail & Related papers  (2024-05-06T16:10:13Z)
• FRAPPE: A Group Fairness Framework for Post-Processing Everything [48.57876348370417]
  We propose a framework that turns any regularized in-processing method into a post-processing approach. We show theoretically and through experiments that our framework preserves the good fairness-error trade-offs achieved with in-processing.
  arXiv  Detail & Related papers  (2023-12-05T09:09:21Z)
• Online Modeling and Monitoring of Dependent Processes under Resource Constraints [11.813520177037763]
  The proposed method designs a collaborative learning-based upper confidence bound (CL-UCB) algorithm to optimally balance the exploitation and exploration of dependent processes under limited resources. efficiency of the proposed method is demonstrated through theoretical analysis, simulation studies and an empirical study of adaptive cognitive monitoring in Alzheimer's disease.
  arXiv  Detail & Related papers  (2023-07-26T14:14:38Z)
• Better Understanding Differences in Attribution Methods via Systematic Evaluations [57.35035463793008]
  Post-hoc attribution methods have been proposed to identify image regions most influential to the models' decisions. We propose three novel evaluation schemes to more reliably measure the faithfulness of those methods. We use these evaluation schemes to study strengths and shortcomings of some widely used attribution methods over a wide range of models.
  arXiv  Detail & Related papers  (2023-03-21T14:24:58Z)
• Intervening With Confidence: Conformal Prescriptive Monitoring of Business Processes [0.7487718119544158]
  This paper proposes an approach to extend existing prescriptive process monitoring methods with predictions with confidence guarantees. An empirical evaluation using real-life public datasets shows that conformal predictions enhance the net gain of prescriptive process monitoring methods under limited resources.
  arXiv  Detail & Related papers  (2022-12-07T15:29:21Z)
• Towards Better Understanding Attribution Methods [77.1487219861185]
  Post-hoc attribution methods have been proposed to identify image regions most influential to the models' decisions. We propose three novel evaluation schemes to more reliably measure the faithfulness of those methods. We also propose a post-processing smoothing step that significantly improves the performance of some attribution methods.
  arXiv  Detail & Related papers  (2022-05-20T20:50:17Z)
• CoCoMoT: Conformance Checking of Multi-Perspective Processes via SMT (Extended Version) [62.96267257163426]
  We introduce the CoCoMoT (Computing Conformance Modulo Theories) framework. First, we show how SAT-based encodings studied in the pure control-flow setting can be lifted to our data-aware case. Second, we introduce a novel preprocessing technique based on a notion of property-preserving clustering.
  arXiv  Detail & Related papers  (2021-03-18T20:22:50Z)
• Explainable Artificial Intelligence for Process Mining: A General Overview and Application of a Novel Local Explanation Approach for Predictive Process Monitoring [0.0]
  This study proposes a conceptual framework sought to establish and promote understanding of decision-making environment. This study defines the local regions from the validation dataset by using the intermediate latent space representations. The adopted deep learning classifier achieves a good performance with the Area Under the ROC Curve of 0.94.
  arXiv  Detail & Related papers  (2020-09-04T10:28:56Z)

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.