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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...

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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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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Releasing Differentially Private Event Logs Using Generative Models

arXiv

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arXiv 2025年

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

关键词： Differential privacy

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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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PMSD: data-driven simulation using system dynamics and process mining

PMSD: Data-driven simulation using system dynamics and proce...

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2020 Best Dissertation Award, Doctoral Consortium, and Demonstration and Resources Track at BPM, BPM-D 2020

作者： Pourbafrani, Mahsa Van Der Aalst, Wil M.P. Chair of Process and Data Science RWTH Aachen University Germany

process mining extends far beyond process discovery and conformance checking, and also provides techniques for bottleneck anal- ysis and organizational mining. However, these techniques are mostly backward-looking. PMSD is a web application tool that supports forward- looking simulation techniques. It transforms the event data and process mining results into a simulation model which can be executed and vali- dated. PMSD includes log transformation, time window selection, rela- tion detection, interactive model generation, simulating and validating the models in the form of system dynamics, i.e., a technique for an ag- gregated simulation. The results of the modules are visualized in the tool for a better interpretation. Copyright © 2020 for this paper by its authors.

关键词： data mining

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Discovering Object-Centric process Simulation Models 5

Discovering Object-Centric Process Simulation Models

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5th International Conference on process Mining, ICPM 2023

作者： Knopp, Benedikt Pourbafrani, Mahsa Van Der Aalst, Wil M. P. Rwth Aachen University Chair of Process and Data Science Aachen Germany

ISBN: (纸本)9798350358391

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. © 2023 IEEE.

关键词： data mining

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Practical aspect of privacy-preserving data publishing in process mining

Practical aspect of privacy-preserving data publishing in pr...

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2020 Best Dissertation Award, Doctoral Consortium, and Demonstration and Resources Track at BPM, BPM-D 2020

作者： Rafiei, Majid Van Der Aalst, Wil M.P. Chair of Process and Data Science RWTH Aachen University Aachen Germany

process mining techniques such as process discovery and con- formance checking provide insights into actual processes by analyzing event data that are widely available in information systems. These data are very valuable, but often contain sensitive information, and process analysts need to balance confidentiality and utility. Privacy issues in pro- cess mining are recently receiving more attention from researchers which should be complemented by a tool to integrate the solutions and make them available in the real world. In this paper, we introduce a Python- based infrastructure implementing state-of-the-art privacy preservation techniques in process mining. The infrastructure provides a hierarchy of usages from single techniques to the collection of techniques, integrated as web-based tools. Our infrastructure manages both standard and non- standard event data resulting from privacy preservation techniques. It also stores explicit privacy metadata to track the modifications applied to protect sensitive data. Copyright © 2020 for this paper by its authors.

关键词： Sensitive data

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Preprocessing event data in process mining 32

Preprocessing event data in process mining

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32nd International Conference on Advanced Information Systems Engineering, CAiSE-DC 2020

作者： Sani, Mohammadreza Fani Process and Data Science Chair RWTH Aachen University Aachen Germany

process mining aims at obtaining insights into business processes by analyzing event data recorded in information systems. Many of current process mining algorithms have difficulties dealing with real event data because of two main reasons. Sometimes noisy and infrequent behavior in event data leads to having complex and incomprehensible results. Furthermore, some process mining algorithms are inapplicable for large event data using normal hardware. In this research, we aim to provide some general preprocessing approaches to deal with the above problems. Using these approaches, we aim to decrease the size and complexity of event data and consequently improve the performance of many process mining algorithms when holding similar results. Some of these approaches are also able to improve the quality of some process mining results. We also discuss some of the challenges of this research. Copyright © 2020 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： data mining

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