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ICPM Demo Track 2019, ICPM Demo Track 2019

作者： Berti, Alessandro Van Zelst, Sebastiaan J. Van Der Aalst, Wil M.P. Gesellschaf, Fraunhofer RWTH Aachen University Process and Data Science group Lehrstuhl für Informatik 9 Aachen 52074 Germany Sankt Augustin United Kingdom

process mining, i.e., a sub-field of data science focusing on the analysis of event data generated during the execution of (business) processes, has seen a tremendous change over the past two decades. Starting off in the early 2000’s, with limited to no tool support, nowadays, several software tools, i.e., both open-source, e.g., ProM and Apromore, and commercial, e.g., Disco, Celonis, processGold, etc., exist. The commercial process mining tools provide limited support for implementing custom algorithms. Moreover, both commercial and open-source process mining tools are often only accessible through a graphical user interface, which hampers their usage in large-scale experimental settings. Initiatives such as RapidProM provide process mining support in the scientific workflow-based data science suite RapidMiner. However, these offer limited to no support for algorithmic customization. In the light of the aforementioned, in this paper, we present a novel process mining library, i.e., process Mining for Python (PM4Py), that aims to bridge this gap, providing integration with state-of-the-art data science libraries, e.g., pandas, numpy, scipy and scikit-learn. We provide a global overview of the architecture and functionality of PM4Py, accompanied by some representative examples of its usage. © 2019 CEUR-WS. All rights reserved.

关键词： Python

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process mining for python (PM4Py): Bridging the gap between process- and data science

arXiv

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arXiv 2019年

作者： Berti, Alessandro Van Zelst, Sebastiaan J. Van Der Aalst, Wil M.P. Rwth Aachen University Process and Data Science Group Lehrstuhl für Informatik 9 Aachen 52074 Germany Sankt Augustin Germany

process mining, i.e., a sub-field of data science focusing on the analysis of event data generated during the execution of (business) processes, has seen a tremendous change over the past two decades. Starting off in the early 2000's, with limited to no tool support, nowadays, several software tools, i.e., both open-source, e.g., ProM and Apromore, and commercial, e.g., Disco, Celonis, processGold, etc., exist. The commercial process mining tools provide limited support for implementing custom algorithms. Moreover, both commercial and open-source process mining tools are often only accessible through a graphical user interface, which hampers their usage in large-scale experimental settings. Initiatives such as RapidProM provide process mining support in the scientific workflow-based data science suite RapidMiner. However, these offer limited to no support for algorithmic customization. In the light of the aforementioned, in this paper, we present a novel process mining library, i.e. process Mining for Python (PM4Py) that aims to bridge this gap, providing integration with state-of-the-art data science libraries, e.g., pandas, numpy, scipy and scikit-learn. We provide a global overview of the architecture and functionality of PM4Py, accompanied by some representative examples of its usage. Copyright © 2019, The Authors. All rights reserved.

关键词： Python

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Root Cause Analysis Using Rule Mining on Object-Centric Event Logs

Root Cause Analysis Using Rule Mining on Object-Centric Eve...

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International Workshops which were held in conjunction with the 6th International Conference on process Mining, ICPM 2024

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

ISBN: (纸本)9783031822247

In business processes, the behavior, evolution and interactions of objects influence the outcome of process instances, and thus the value that a business user may assign to them. For example, in an order-to-cash process, a complete and timely delivery of a package is desirable, but depends on what happens to other objects upstream, like production batches. Negative outcomes call for a Root Cause Analysis (RCA) on the process. While many approaches for RCA using process mining exist, none is native to object-centric frameworks and thus suitable for capturing dependencies across object types. This work presents a method for RCA that operates on object-centric event logs (OCELs). Given an OCEL, our method returns a set of association rules on the activity level. These rules associate descriptive patterns over the various object types occurring at events with patterns indicating the process outcome. The patterns are abstracted from the log with the help of a first-order logic based query engine. A case study confirmed that our method can identify problematic interactions across various object types in real-life business processes. © The Author(s) 2025.

关键词： Mine safety

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Object-Centric Local process Models

Object-Centric Local Process Models

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International Workshops which were held in conjunction with the 6th International Conference on process Mining, ICPM 2024

作者： Peeva, Viki Porsil, Marvin van der Aalst, Wil M. P. Chair of Process and Data Science RWTH Aachen University Aachen Germany

ISBN: (纸本)9783031822247

process mining is a technology that helps understand, analyze, and improve processes. It has been present for around two decades, and although initially tailored for business processes, the spectrum of analyzed processes nowadays is evermore growing. To support more complex and diverse processes, subdisciplines such as object-centric process mining and behavioral pattern mining have emerged. Behavioral patterns allow for analyzing parts of the process in isolation, while object-centric process mining enables combining different perspectives of the process. In this work, we introduce Object-Centric Local process Models (OCLPMs). OCLPMs are behavioral patterns tailored to analyzing complex processes where no single case notion exists and we leverage object-centric Petri nets to model them. Additionally, we present a discovery algorithm that starts from object-centric event logs, and implement the proposed approach in the open-source framework ProM. Finally, we demonstrate the applicability of OCLPMs in two case studies and evaluate the approach on various event logs. © The Author(s) 2025.

关键词： Petri nets

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

PM-LLM-Benchmark: Evaluating Large Language Models on Proce...

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International Workshops which were held in conjunction with the 6th International Conference on process Mining, ICPM 2024

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

ISBN: (纸本)9783031822247

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. © The Author(s) 2025.

关键词： Benchmarking

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BESTAnP: Bi-Step Efficient and Statistically Optimal Estimator for Acoustic-n-Point Problem

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IEEE Robotics and Automation Letters 2025年第6期10卷 5313-5320页

作者： Sheng, Wenliang Zhao, Hongxu Chen, Lingpeng Zeng, Guangyang Shao, Yunling Hong, Yuze Yang, Chao Hong, Ziyang Wu, Junfeng East China University of Science and Technology Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education Department of Automation Shanghai200237 China Chinese University of Hong Kong School of Data Science Shenzhen518172 China

We consider the acoustic-n-point (AnP) problem, which estimates the pose of a 2D forward-looking sonar (FLS) according to n 3D-2D point correspondences. We explore the nature of the measured partial spherical coordinates and reveal their inherent relationships to translation and orientation. Based on this, we propose a bi-step efficient and statistically optimal AnP (BESTAnP) algorithm that decouples the estimation of translation and orientation. Specifically, in the first step, the translation estimation is formulated as the range-based localization problem based on distance-only measurements. In the second step, the rotation is estimated via eigendecomposition based on azimuth-only measurements and the estimated translation. BESTAnP is the first AnP algorithm that gives a closed-form solution for the full 6 °-of-freedom (DoF) pose. In addition, we conduct bias elimination for BESTAnP such that it owns the statistical property of consistency. Through simulation and real-world experiments, we demonstrate that compared with the state-of-the-art (SOTA) methods, BESTAnP is over ten times faster and features real-time capacity in resource-constrained platforms while exhibiting comparable accuracy. Moreover, we embed BESTAnP into a single sonar-based odometry which shows its effectiveness for trajectory estimation. © 2016 IEEE.

关键词： Sonar

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Releasing differentially private event logs using generative models

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data and Knowledge Engineering 2025年 159卷

作者： Wangelik, Frederik Rafiei, Majid Pourbafrani, Mahsa van der Aalst, Wil M.P. Chair of Process and Data Science RWTH Aachen University Aachen Germany

In recent years, the industry has been witnessing an extended usage of process mining and automated event data analysis. Consequently, there is a rising significance in addressing privacy apprehensions related to the inclusion of sensitive and private information within event data utilized by process mining algorithms. State-of-the-art research mainly focuses on providing quantifiable privacy guarantees, e.g., via differential privacy, for trace variants that are used by the main process mining techniques, e.g., process discovery. However, privacy preservation techniques designed for the release of trace variants are still insufficient to meet all the demands of industry-scale utilization. Moreover, ensuring privacy guarantees in situations characterized by a high occurrence of infrequent trace variants remains a challenging endeavor. In this paper, we introduce two novel approaches for releasing differentially private trace variants based on trained generative models. With TraVaG, we leverage Generative Adversarial Networks (GANs) to sample from a privatized implicit variant distribution. Our second method employs Denoising Diffusion Probabilistic Models that reconstruct artificial trace variants from noise via trained Markov chains. Both methods offer industry-scale benefits and elevate the degree of privacy assurances, particularly in scenarios featuring a substantial prevalence of infrequent variants. Also, they overcome the shortcomings of conventional privacy preservation techniques, such as bounding the length of variants and introducing fake variants. Experimental results on real-life event data demonstrate that our approaches surpass state-of-the-art techniques in terms of privacy guarantees and utility preservation. © 2025 The Authors

关键词： Generative adversarial networks

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Federated Conformance Checking

arXiv

引用

arXiv 2025年

作者： Rafiei, Majid Pourbafrani, Mahsa van der Aalst, Wil M.P. Process and Data Science RWTH Aachen University Aachen Germany

Conformance checking is a crucial aspect of process mining, where the main objective is to compare the actual execution of a process, as recorded in an event log, with a reference process model, e.g., in the form of a Petri net or a BPMN. Conformance checking enables identifying deviations, anomalies, or non-compliance instances. It offers different perspectives on problems in processes, bottlenecks, or process instances that are not compliant with the model. Performing conformance checking in federated (inter-organizational) settings allows organizations to gain insights into the overall process execution and to identify compliance issues across organizational boundaries, which facilitates process improvement efforts among collaborating entities. In this paper, we propose a privacy-aware federated conformance-checking approach that allows for evaluating the correctness of overall cross-organizational process models, identifying miscommunications, and quantifying their costs. For evaluation, we design and simulate a supply chain process with three organizations engaged in purchase-to-pay, order-to-cash, and shipment processes. We generate synthetic event logs for each organization as well as the complete process, and we apply our approach to identify and evaluate the cost of pre-injected miscommunications. © 2025, CC BY.

关键词： Supply chains

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XPM: eXplainable process Mining 3rd

XPM: eXplainable Process Mining

引用

3rd International Conference on Advances in Information and Communication Technology, ICTA 2024

作者： Kim, Kwanghoon Pio CCSRI Kyonggi University Suwon Korea Republic of Data. Process Engineering Research Lab. Div. of AI Computer Science and Engineering Kyonggi University Gyeonggi Suwon16227 Korea Republic of

ISBN: (纸本)9783031809422

This keynote speech is to all intents and purposes introducing a new process mining approach and its implemented system, which are named as eXplainable process mining (XPM) approach and system, respectively. Through the XPM approach, we can selectively amalgamate a group of process event log traces, and through the XPM system, we can ultimately discover a series of complete and safe process models from a process enactment event log dataset. The XPM approach is theoretically based on the typical process modeling methodology of structured information control nets (abbreviated as SICN) and its algorithmic functionality is substantiated by the explainable ρ-Algorithm that is extended from the original ρ-Algorithm [6]. This keynote speech is also explicating the proven process mining algorithm that is able to discover the underlying SICN-oriented process model from a dataset of process enactment event logs and histories. More specifically, the concrete goal of this keynote speech is about an explainable way of discovering the complete structural formation of SICN-oriented process model. Assume that the structural formation is made up of an arbitrary number of combinational building blocks of the primitive process patterns such as linear (sequential), disjunctive (exclusive-OR), conjunctive (parallel-AND), and repetitive (iterative-LOOP) process patterns. Conclusively, it would be emphasized that the XPM approach and system ought to be very effective and well-fitted for managing life-cycles of very large-scale process models deployed in process-aware enterprises and organizations. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Information management

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Multi-agent Scheduling for Shared Manufacturing Systems 6

Multi-agent Scheduling for Shared Manufacturing Systems

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6th International Conference on Industry 4.0 and Smart Manufacturing, ISM 2024

作者： Duran, Ege Ozturk, Cemalettin O'Sullivan, Barry SFI Centre for Research Training in AI School of Computer Science IT University College Cork Ireland Department of Process Energy & Transport Engineering Munster Technological University Cork Ireland Insight SFI Research Centre for Data Analytics School of Computer Science & IT University College Cork Ireland

Advances in digitization and resource-sharing business models have created new opportunities for manufacturing companies, enhancing competitiveness and resilience. However, these benefits bring computational challenges in efficiently planning and scheduling shared resources. Therefore, there is a need for scalable and quick solutions for practical applications. Shared manufacturing systems share characteristics with parallel machine scheduling, allowing for the application of advancements from this domain. This research focuses on Multi-Agent Parallel Machine Scheduling (MAPMS) in shared manufacturing, specifically addressing scenarios involving two parallel machines and distinct agents managing exclusive, set of non-overlapping orders. The study introduces a novel multi-objective mixed integer programming (MIP) model for order scheduling across multiple facilities, accounting for sequence-dependent setup times between orders. It also proposes a new heuristic method designed for industrial use. Benchmark instances demonstrate the practicality of both the MIP model and heuristic, contributing valuable insights into MAPMS challenges in shared manufacturing environments. © 2025 The Authors.

关键词： Mixed-integer linear programming

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