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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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Analyzing data Streams from Cyber-Physical-Systems: A Case Study

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SN Computer science 2024年第6期5卷 706页

作者： Beyel, Harry H. Makke, Omar Pourbafrani, Mahsa Gusikhin, Oleg van der Aalst, Wil M. P. Chair of Process and Data Science RWTH Aachen University Global Data Insight & amp Analytics Ford

We show that conducting a process-mining-centric analysis concerning cyber-physical systems provides insights into usage behavior. To show that, we perform our analysis on connected-vehicle data. We transform connected-vehicle data into an event log. We analyze the resulting event log using various process-mining techniques. In particular, we apply basic statistical analysis as well as process-discovery and conformance-checking techniques to receive a well-representative process model. We apply various process-enhancement techniques to get deeper insights. Finally, we capture a multi-perspective view using a state-based approach. We show deviations between a de-jure model and our picked process model, leading to better knowledge concerning real user behavior. We observed that the predefined escalation of warning states does not happen. Additionally, we verified system requirements. Furthermore, we show that the reasons for drivers’ behavior are not related to system issues. Applying process-mining techniques to data concerning cyber-physical systems provides valuable insights into their functionality in a real-world setting. By utilizing process-mining techniques, we can extract insights to a human-understandable level and provide a well-studied access point. © The Author(s) 2024.

关键词： Connected vehicles Cyber-physical systems Internet of Things process mining

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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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ESCal: Efficient and Scalable Calibration of Camera Networks Using a Top View 25

ESCal: Efficient and Scalable Calibration of Camera Networks...

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25th International Conference on Digital Image Computing: Techniques and Applications, DICTA 2024

作者： Ziegler, Tobias Müller, Marcel Khelil, Abdelmajid Institute for Data and Process Science Landshut University of Applied Sciences Germany

ISBN: (纸本)9798350379037

Calibrating camera networks is a complex yet crucial task for ensuring accurate data collection, particularly for large-scale scenarios. This work's main contribution is a method for efficiently estimating the network's extrinsic paramaters for every camera. It also determines their effective coverage without requiring overlapping fields of view. We propose ESCal, an efficient and scalable solution that leverages homographies and point correspondences while disregarding their prerequisite of planar scenes. It semi-automatically determines the pose of the considered cameras given one top view of the scene. Our approach is designed to be fast, easy to use, scalable, and sufficiently accurate. We demonstrate these qualities under real-world conditions. © 2024 IEEE.

关键词： Cameras

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Maximizing Reuse and Interoperability in Industry 4.0 with a Minimal data Exchange Format for Machine data

Maximizing Reuse and Interoperability in Industry 4.0 with a...

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Modellierung 2024

作者： Unterberg, Leah Tacke Genannt Koren, István van der Aalst, Wil M.P. Chair of Process and Data Science RWTH Aachen University Aachen Germany

ISBN: (纸本)9783885797425

data interoperability in Industry 4.0 is a continuous challenge for industry and research. Many organizations face the challenge of managing data lakes that, without proper governance, risk becoming disorganized ‘data swamps’ with disparate data models and formats. This heterogeneity leads to inefficient data utilization. Standardization efforts have produced suites of extensive models as they try to accommodate diverse requirements while still being comprehensive. Their complexity has hindered their adoption. To address this, we propose a minimal intermediate meta model for a frequently considered type of data in smart manufacturing, namely Machine data. This type of data is central to industrial IoT platforms and research efforts on Digital Shadows & Twins. It encompasses raw time series and event data from sensors and digital controllers. This model-in-the-middle is intended to bridge the gap between heterogeneous source systems and highly structured and semantically clean input for data science techniques. To be broadly applicable, it has to be minimal and favor abstraction over details. We equip it with a standardized exchange format based on CSV, which reduces friction in data sharing. Furthermore, we provide a precise mathematical formalization that connects it to the language of data science methods. This enables the generic implementation of methods that can easily be reused and combined. Finally, we validate the model together with initial tool support in the large-scale cluster of excellence Internet of Production (IoP). We conclude that it is possible and feasible to accelerate the realization of the ambitions for the future of manufacturing using such minimal models. © 2024 Gesellschaft fur Informatik (GI). All rights reserved.

关键词： Smart manufacturing

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SLURMminer: A Tool for SLURM System Analysis with process Mining 21

SLURMminer: A Tool for SLURM System Analysis with Process Mi...

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Dissertation Award, Doctoral Consortium, and Demonstration and Resources Forum at the 21st International Conference on Business process Management, BPM-D 2023

作者： Sadeghibogar, Zahra Berti, Alessandro Pegoraro, Marco van der Aalst, Wil M.P. Process and Data Science RWTH Aachen University Aachen Germany

SLURMminer is a tool designed to analyze SLURM systems in High-Performance Computing (HPC) clusters. It utilizes process mining techniques to generate event logs, extract process models, and visualize critical business intelligence metrics. The tool's unique log extraction approach for SLURM clusters allows for a detailed analysis of jobs and workflows. By providing insights into workflow execution, workload patterns, system utilization, workload balancing, and anomaly detection, SLURMminer equips researchers and system administrators with essential data for performance assessment and user behavior analysis. This paper details installation procedures and describes the tool's functionalities, facilitating efficient management and optimization of HPC clusters with SLURMminer. © 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0). CEUR Workshop Proceedings (***)

关键词： Anomaly detection

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A Collection of Simulated Event Logs for Fairness Assessment in process Mining 21

A Collection of Simulated Event Logs for Fairness Assessment...

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Dissertation Award, Doctoral Consortium, and Demonstration and Resources Forum at the 21st International Conference on Business process Management, BPM-D 2023

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

Fairness in process mining is vital for data-based decisions, but progress is hampered by limited event data accounting for fairness. We're offering a set of simulated event logs covering four key areas, showcasing diverse discrimination scenarios. Simulated with CPN Tools, these logs provide known ground truth, serving as a reliable base for fairness assessment. Freely available under the CC-BY-4.0 license and XES standard, they're compatible with most process mining tools. Our aim is to facilitate research on fairness in process mining, fostering equitable data-based decision-making. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0). CEUR Workshop Proceedings (***)

关键词： Decision making

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Modeling process mining in agile project management using Scrum as an example 37

Modeling process mining in agile project management using Sc...

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37. Jahrestagung des Arbeitskreises Wirtschaftsinformatik an HAW, AKWI 2024 - 37th Annual Conference of the Working Group on Business Informatics at Universities of Applied sciences, AKWI 2024

作者： Schwägerl, Pia Schidek, Anna Timinger, Holger Martens, Maren Institute for Data and Process Science Hochschule Landshut Am Lurzenhof 1 Landshut84036 Germany

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