Sustainability Analysis Patterns for Process Mining and Process Modelling Approaches
- URL: http://arxiv.org/abs/2503.13584v1
- Date: Mon, 17 Mar 2025 17:50:21 GMT
- Title: Sustainability Analysis Patterns for Process Mining and Process Modelling Approaches
- Authors: Andreas Fritsch,
- Abstract summary: One of the main challenges of existing Sustainable BPM approaches is the lack of a sound conception of sustainability impacts.<n>This paper describes a set of sustainability analysis patterns that integrate BPM concepts with concepts from existing sustainability analysis methods.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Business Process Management (BPM) has the potential to help companies manage and reduce their activities' negative social and environmental impacts. However, so far, only limited capabilities for analysing the sustainability impacts of processes have been integrated into established BPM methods and tools. One of the main challenges of existing Sustainable BPM approaches is the lack of a sound conception of sustainability impacts. This paper describes a set of sustainability analysis patterns that integrate BPM concepts with concepts from existing sustainability analysis methods to address this challenge. The patterns provide a framework to evaluate and develop process modelling and process mining approaches for discovering, analysing and improving the sustainability impacts of processes. It is shown how the patterns can be used to evaluate existing process modelling and process mining approaches.
Related papers
- The Lessons of Developing Process Reward Models in Mathematical Reasoning [62.165534879284735]
Process Reward Models (PRMs) aim to identify and mitigate intermediate errors in the reasoning processes.<n>We develop a consensus filtering mechanism that effectively integrates Monte Carlo (MC) estimation with Large Language Models (LLMs)<n>We release a new state-of-the-art PRM that outperforms existing open-source alternatives.
arXiv Detail & Related papers (2025-01-13T13:10:16Z) - 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) - Unraveling the Never-Ending Story of Lifecycles and Vitalizing Processes [2.474551220017185]
We show the existence of lifecycle processes in many industries and that their appropriate conceptualizations pave the way for the definition of suitable modeling and analysis techniques.
This paper provides a set of requirements for their analysis, and a conceptualization of lifecycle and vitalizing processes.
arXiv Detail & Related papers (2024-07-25T08:52:23Z) - MR-Ben: A Meta-Reasoning Benchmark for Evaluating System-2 Thinking in LLMs [55.20845457594977]
Large language models (LLMs) have shown increasing capability in problem-solving and decision-making.
We present a process-based benchmark MR-Ben that demands a meta-reasoning skill.
Our meta-reasoning paradigm is especially suited for system-2 slow thinking.
arXiv Detail & Related papers (2024-06-20T03:50:23Z) - SOPA: A Framework for Sustainability-Oriented Process Analysis and Re-design in Business Process Management [0.08192907805418582]
We propose and study SOPA, a framework for sustainability-oriented process analysis and re-design.<n> SOPA extends the BPM life cycle by use of Life Cycle Assessment (LCA) for sustainability analysis in combination with Activity-based Costing (ABC)
arXiv Detail & Related papers (2024-05-02T11:02:23Z) - 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) - Learning Planning-based Reasoning by Trajectories Collection and Process Reward Synthesizing [61.98556945939045]
We propose a framework to learn planning-based reasoning through Direct Preference Optimization (DPO) on collected trajectories.
Our results on challenging logical reasoning benchmarks demonstrate the effectiveness of our learning framework.
arXiv Detail & Related papers (2024-02-01T15:18:33Z) - Extending Process Discovery with Model Complexity Optimization and
Cyclic States Identification: Application to Healthcare Processes [62.997667081978825]
The paper presents an approach to process mining providing semi-automatic support to model optimization.
A model simplification approach is proposed, which essentially abstracts the raw model at the desired granularity.
We aim to demonstrate the capabilities of the technological solution using three datasets from different applications in the healthcare domain.
arXiv Detail & Related papers (2022-06-10T16:20:59Z) - Prescriptive Process Monitoring: Quo Vadis? [64.39761523935613]
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.
arXiv Detail & Related papers (2021-12-03T08:06:24Z) - Automated Sustainability Compliance Checking Using Process Mining and
Formal Logic [0.0]
I want to contribute to the application of compliance checking techniques for the purpose of sustainability compliance.
I want to analyse and develop data-driven approaches, which allow to automate the task of compliance checking.
arXiv Detail & Related papers (2020-06-10T11:07:57Z)
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.