Process science最新文献

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When a model gives you mixed signals: cognitive effects and visual behavior. 当一个模型给你混合的信号：认知效果和视觉行为。

Process science Pub Date : 2025-01-01 Epub Date: 2025-10-06 DOI: 10.1007/s44311-025-00022-8

Amine Abbad-Andaloussi, Marco Franceschetti, Hugo A López, Clemens Schreiber, Barbara Weber

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引用次数: 0

A ground truth approach for assessing process mining techniques. 一种评估过程挖掘技术的基础真实度方法。

Process science Pub Date : 2025-01-01 Epub Date: 2025-03-20 DOI: 10.1007/s44311-025-00006-8

Dominique Sommers, Natalia Sidorova, Boudewijn van Dongen

{"title":"A ground truth approach for assessing process mining techniques.","authors":"Dominique Sommers, Natalia Sidorova, Boudewijn van Dongen","doi":"10.1007/s44311-025-00006-8","DOIUrl":"10.1007/s44311-025-00006-8","url":null,"abstract":"The assessment of process mining techniques using real-life data is often compromised by the lack of ground truth knowledge, the presence of non-essential outliers in system behavior and recording errors in event logs. Using synthetically generated data could leverage ground truth for better evaluation. Existing log generation tools inject noise directly into the logs, which does not capture many typical behavioral deviations. Furthermore, the link between the model and the log, which is needed for later assessment, becomes lost. We propose a ground-truth approach for generating process data from existing or synthetic initial process models, whether automatically generated or hand-made. This approach incorporates patterns of behavioral deviations and recording errors to produce a synthetic yet realistic deviating model and imperfect event log. These, together with the initial model, are required to assess process mining techniques based on ground truth knowledge. We demonstrate this approach to create datasets of synthetic process data for three processes, one of which we used in a conformance checking use case, focusing on the assessment of (relaxed) systemic alignments to expose and explain deviations in modeled and recorded behavior. Our results show that this approach, unlike traditional methods, provides detailed insights into the strengths and weaknesses of process mining techniques, both quantitatively and qualitatively.","PeriodicalId":520481,"journal":{"name":"Process science","volume":"2 1","pages":"1"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11934509/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143712420","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0