检索结果-内蒙古大学图书馆

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

来源：评论

学校读者我要写书评

暂无评论

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

Object-Centric Local process Models

Object-Centric Local Process Models

引用

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

来源：评论

学校读者我要写书评

暂无评论

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...

引用

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

来源：评论

学校读者我要写书评

暂无评论

Discovering Object-Centric process Simulation Models 5

Discovering Object-Centric Process Simulation Models

引用

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

来源：评论

学校读者我要写书评

暂无评论

TraVaG: Differentially Private Trace Variant Generation Using GANs 17th

TraVaG: Differentially Private Trace Variant Generation Usin...

引用

17th International Conference on Research Challenges in Information sciences, RCIS 2023

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

ISBN: (纸本)9783031330797

process mining is rapidly growing in the industry. Consequently, privacy concerns regarding sensitive and private information included in event data, used by process mining algorithms, are becoming increasingly relevant. State-of-the-art research mainly focuses on providing privacy guarantees, e.g., differential privacy, for trace variants that are used by the main process mining techniques, e.g., process discovery. However, privacy preservation techniques for releasing trace variants still do not fulfill all the requirements of industry-scale usage. Moreover, providing privacy guarantees when there exists a high rate of infrequent trace variants is still a challenge. In this paper, we introduce TraVaG as a new approach for releasing differentially private trace variants based on Generative Adversarial Networks (GANs) that provides industry-scale benefits and enhances the level of privacy guarantees when there exists a high ratio of infrequent variants. Moreover, TraVaG overcomes shortcomings of conventional privacy preservation techniques such as bounding the length of variants and introducing fake variants. Experimental results on real-life event data show that our approach outperforms state-of-the-art techniques in terms of privacy guarantees, plain data utility preservation, and result utility preservation. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： data mining

来源：评论

学校读者我要写书评

暂无评论

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

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

CSV-PM-LLM-Parsing: Automatic Ingestion of CSV Event Logs for process Mining using LLMs 22

CSV-PM-LLM-Parsing: Automatic Ingestion of CSV Event Logs fo...

引用

Best Dissertation Award, Doctoral Consortium, and Demonstration and Resources Forum at 22nd International Conference on Business process Management, BPM-D 2024

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

This demo paper introduces CSV-PM-LLM-Parsing, a Python library to automatically transform unstructured event logs contained in CSV files into process mining event logs by using Large Language Models (LLMs). In particular, we implement modules for three problems: 1) identification of the separator and quote character, 2) selection of the essential process mining columns, and 3) timestamp format detection. Our library is compatible with any LLM supporting the OpenAI’s API specification. Our experiment shows that some LLMs (such as gpt-4o or the open-source Qwen/Qwen2-72B-Instruct) can effectively tackle all three problems. The source code is publicly available in the Git repository https://***/fit-alessandro-berti/csv-pm-llm-parsing. © 2024 Copyright for this paper by its authors.

关键词： Anomaly detection

来源：评论

学校读者我要写书评

暂无评论

Explainable Object-Centric Anomaly Detection: the Role of Domain Knowledge 22

Explainable Object-Centric Anomaly Detection: the Role of Do...

引用

Best Dissertation Award, Doctoral Consortium, and Demonstration and Resources Forum at 22nd International Conference on Business process Management, BPM-D 2024

作者： Berti, Alessandro Jessen, Urszula van der Aalst, Wil M.P. Fahland, Dirk Process and Data Science Chair RWTH Aachen University Aachen Germany Fraunhofer FIT Sankt Augustin Germany Process Insights ECE Group Services Hamburg Germany Eindhoven University of Technology Netherlands

Anomaly detection is used in process mining to identify behavior differing significantly from the other instances. However, providing actionable insights out of the raw scores is challenging. In this paper, we propose three methodologies for explainable anomaly detection. In particular, we focus on object-centric event data as it increases the dimensions for anomaly detection, including the lifecycle of different objects and the interactions between them. Two of the proposed methodologies rely on the provision of domain knowledge, which can also be provided by Large Language Models (LLMs). We test the proposed techniques in a real-life case study on an (object-centric) ERP process. © 2024 Copyright for this paper by its authors.

关键词： Domain Knowledge

来源：评论

学校读者我要写书评

暂无评论

Analyzing Cyber-Physical Systems in Cars: A Case Study

Analyzing Cyber-Physical Systems in Cars: A Case Study

引用

12th International Conference on data science, Technology and Applications, data 2023

作者： Beyel, Harry H. Makke, Omar Yuan, Fangbo Gusikhin, Oleg van der Aalst, Wil M.P. Chair of Process and Data Science RWTH Aachen University Aachen Germany Global Data Insight & Analytics Ford Dearborn United States

ISBN: (纸本)9789897586644

Cyber-physical systems connected to the internet are generating unprecedented volumes of data. Understanding cyber-physical systems’ behavior using collected data is becoming increasingly important. process-mining techniques consider sequences of events and thus can be used to check and verify how such cyber-physical systems operate. The data captured by cyber-physical systems are typically noisy and are not readily suitable for process mining. In this work, we present how a stream of connected-vehicle data can be transformed into an event log suitable for process mining. By applying different process-discovery techniques, we discover de-facto models that capture the behavior of an assistance system embedded in cars. We apply conformance-checking techniques and consult domain experts to find the best de-facto model. In addition, we apply conformance-checking methods to a preexisting, de-jure model that we transformed into a Petri net. We compare both models and point out differences. In this process, we show how we overcome challenges and highlight why applying process-mining techniques in the cyber-physical systems domain is valuable. © 2023 by SCITEPRESS–science and Technology Publications, Lda.

关键词： Cybersecurity

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：