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Discovering Object-Centric process Simulation Models

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Discovering Object-Centric Process Simulation Models

International Conference on process Mining (ICPM)

作者： Benedikt Knopp Mahsa Pourbafrani Wil M. P. van der Aalst Chair of Process and Data Science RWTH Aachen University Aachen Germany

process simulation assesses the impact of changing environmental parameters on a process. To obtain realistic simulation models, process mining techniques can be deployed for a log-based discovery. Such discovery techniques usually rely on a fixed case notion, falling short in capturing the entangled nature of real organizational processes as an interplay of objects and subprocesses. Yet there is a need for such methods, given the requirement for information systems to foresee and adapt to changing environments in an online setting and in a holistic manner. In this work, we approach this research need by elaborating a method for simulation model discovery that is based on the object-centricity paradigm. To implement object-centric simulation, some intrinsic challenges have to be overcome. These include, first, the parametrizable generation of sets of objects having predefined interrelations that structure possible behavior. Second, the generated objects have to be synchronized and routed through a control-flow model. We outline these challenges, describe our solution approach, and evaluate the quality of both object generation and behavior.

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A Collection of Simulated Event Logs for Fairness Assessment in process Mining

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arXiv 2023年

作者： Pohl, Timo Berti, Alessandro Qafari, Mahnaz Sadat van der Aalst, Wil M.P. Process and Data Science Chair RWTH Aachen University Aachen Germany

The analysis of fairness in process mining is a significant aspect of data-driven decision-making, yet the advancement in this field is constrained due to the scarcity of event data that incorporates fairness considerations. To bridge this gap, we present a collection of simulated event logs, spanning four critical domains, which encapsulate a variety of discrimination scenarios. By simulating these event logs with CPN Tools, we ensure data with known ground truth, thereby offering a robust foundation for fairness analysis. These logs are made freely available under the CC-BY-4.0 license and adhere to the XES standard, thereby assuring broad compatibility with various process mining tools. This initiative aims to empower researchers with the requisite resources to test and develop fairness techniques within process mining, ultimately contributing to the pursuit of equitable, data-driven decision-making processes. © 2023, CC BY.

关键词： Decision making

ReThink Your processes! A Review of process Mining for Sustainability

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ReThink Your Processes! A Review of Process Mining for Susta...

ICT for Sustainability (ICT4S), International Conference on

作者： Nina Graves István Koren Wil M.P. van der Aalst Chair of Process and Data Science (PADS) RWTH Aachen University Aachen Germany

The transition towards more sustainable practices requires companies to assess their impact on the social and ecological environment and establish new processes in complex inter-organisational systems. process mining is a collection of data-driven techniques to visualise, analyse and improve business processes. Its potential to increase sustainable business processes has been acknowledged by academia and industry but not systematically reviewed. This work analyses process mining's application for sustainability by conducting two consecutive literature studies - the first on the broad domain of sustainability, and the second on the circular economy, a widely accepted approach for pursuing sustainability. Results show the potential of process mining for assessing and analysing sustainability in business processes, allowing for data-driven decision support and targeted improvement. They also show that process mining has yet to reach the sustainability domain. To enable collaboration between both communities, we present PM4S, a framework for applying process mining for sustainability.

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Causal Inference in ProM 3

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Causal Inference in ProM

3rd International Conference on process Mining Doctoral Consortium and Demo Track, ICPM-D 2021

作者： Qafari, Mahnaz Sadat van der Aalst, Wil M.P. Chair of Process and Data Science RWTH Aachen University Aachen Germany

process mining is widely used to turn the stored data by the information systems of companies into actionable information. Companies are not just interested in discovering their processes but also want to know how to enhance them. Thus, they are interested not just in detecting the performance and conformance problems in their processes, but also in designing specific action steps to reengineer their processes. Knowing the causal relationships among the process features is vital information that may help to improve a process. In this paper, we present a ProM plug-in that helps process owners discover the causal relationships among the features of their processes and also provide them with the possibility of foreseeing the effect of interventions on their processes. Copyright © 2021 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Technology transfer

PM-LLM-Benchmark: Evaluating Large Language Models on process Mining Tasks

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arXiv 2024年

作者： Berti, Alessandro Kourani, Humam van der Aalst, Wil M.P. Process and Data Science Chair RWTH Aachen University Aachen Germany Fraunhofer FIT Sankt Augustin Germany

Large Language Models (LLMs) have the potential to semi-automate some process mining (PM) analyses. While commercial models are already adequate for many analytics tasks, the competitive level of open-source LLMs in PM tasks is unknown. In this paper, we propose PM-LLM-Benchmark, the first comprehensive benchmark for PM focusing on domain knowledge (process-mining-specific and process-specific) and on different implementation strategies. We focus also on the challenges in creating such a benchmark, related to the public availability of the data and on evaluation biases by the LLMs. Overall, we observe that most of the considered LLMs can perform some process mining tasks at a satisfactory level, but tiny models that would run on edge devices are still inadequate. We also conclude that while the proposed benchmark is useful for identifying LLMs that are adequate for process mining tasks, further research is needed to overcome the evaluation biases and perform a more thorough ranking of the "competitive" LLMs. © 2024, CC BY.

关键词： data mining

Evaluating Large Language Models in process Mining: Capabilities, Benchmarks, and Evaluation Strategies

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arXiv 2024年

作者： Berti, Alessandro Kourani, Humam Häfke, Hannes Li, Chiao-Yun Schuster, Daniel Fraunhofer FIT Sankt Augustin Germany Process and Data Science Chair RWTH Aachen University Aachen Germany

Using Large Language Models (LLMs) for process Mining (PM) tasks is becoming increasingly essential, and initial approaches yield promising results. However, little attention has been given to developing strategies for evaluating and benchmarking the utility of incorporating LLMs into PM tasks. This paper reviews the current implementations of LLMs in PM and reflects on three different questions. 1) What is the minimal set of capabilities required for PM on LLMs? 2) Which benchmark strategies help choose optimal LLMs for PM? 3) How do we evaluate the output of LLMs on specific PM tasks? The answer to these questions is fundamental to the development of comprehensive process mining benchmarks on LLMs covering different tasks and implementation paradigms. © 2024, CC BY.

关键词： Benchmarking

***: a Comprehensive Module for Implementing PM on LLMs

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arXiv 2024年

作者： Berti, Alessandro Process and Data Science Chair RWTH Aachen University Aachen Germany Fraunhofer FIT Sankt Augustin Germany

pm4py is a process mining library for Python implementing several process mining (PM) artifacts and algorithms. It also offers methods to integrate PM with large language models (LLMs). This paper examines how the current paradigms of PM on LLM are implemented in pm4py, identifying challenges such as privacy, hallucinations, and the context window limit. © 2024, CC BY.

关键词： Python

Discovering high-quality process models despite data scarcity 42

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Discovering high-quality process models despite data scarcit...

42nd International Conference on Conceptual Modeling: ER Forum, 7th Symposium on Conceptual Modeling Education, SCME 2023, Project Exhibitions, Posters and Demos, and Doctoral Consortium, ER-Companion 2023

作者： Adams, Jan Niklas Peeperkorn, Jari Brockhoff, Tobias Terrier, Isabelle Göhner, Heiko Uysal, Merih Seran Broucke, Seppe vanden De Weerdt, Jochen van der Aalst, Wil M.P. Chair of Process and Data Science RWTH Aachen University Aachen Germany KU Leuven Leuven Belgium Heidelberger Druckmaschinen AG Heidelberg Germany Department of Business Informatics and Operations Management Ghent University Ghent Belgium

process discovery algorithms learn process models from executed activity sequences, describing concurrency, causality, and conflict. Concurrent activities require observing multiple permutations, increasing data requirements, especially for processes with concurrent subprocesses such as hierarchical, composite, or distributed processes. While process discovery algorithms traditionally use sequences of activities as input, recently introduced object-centric process discovery algorithms can use graphs of activities as input, encoding partial orders between activities. As such, they contain the concurrency information of many sequences in a single graph. In this paper, we address the research question of reducing process discovery data requirements when using object-centric event logs for process discovery. We classify different real-life processes according to the control-flow complexity within and between subprocesses and introduce an evaluation framework to assess process discovery algorithm quality of traditional and object-centric process discovery based on the sample size. We complement this with a large-scale production process case study. Our results show reduced data requirements, enabling the discovery of large, concurrent processes such as manufacturing with little data, previously infeasible with traditional process discovery. Our findings suggest that object-centric process mining could revolutionize process discovery in various sectors, including manufacturing and supply chains. © 2023 CEUR-WS. All rights reserved.

关键词： Object-Centric process Mining process Discovery process Mining

Vision Paper: Leveraging Industrial Big data - Past, Present, and Future of the World Wide Lab

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Vision Paper: Leveraging Industrial Big Data - Past, Present...

2023 IEEE International Conference on Big data, Bigdata 2023

作者： Behery, Mohamed Glawe, Felix Koren, Istvan Ziefle, Martina Lakemeyer, Gerhard Brauner, Philipp Rwth Aachen University Knowledge-Based Systems Group Aachen Germany Rwth Aachen University Human Computer Interaction Center Aachen Germany Rwth Aachen University Chair of Process and Data Science Aachen Germany

ISBN: (纸本)9798350324457

Many initiatives aim at answering the challenges of the fourth industrial revolution (Industry 4.0). A key aspect of these initiatives is the connectivity of the different industrial entities. Communication and information exchange between entities usually flows in two dimensions;a vertical one, that spans the different levels of abstraction within the company, and a horizontal one connecting the different entities in the supply chain. In contrast, the Internet of Production (IoP) presents the World Wide Lab (WWL) allowing an extra dimension of communication, which we call the cross-company dimension. Hence, the WWL integrates the different entities within and across potentially competing labs and companies. The WWL leverages and aggregates accessible production data fostering an exchange of information, that prevents redundant experimentation and increases quality of data-driven models by including data from diverse manufacturing settings. This paper discusses work done towards this vision and technical, economic, social, legal, and ethical challenges the WWL faces and provides a roadmap towards infrastructuring the WWL. © 2023 IEEE.

关键词： Digital Shadows Industrial data Space Industrial Internet of Things Internet of Production World Wide Lab