Pub Date : 2025-01-01Epub Date: 2025-10-06DOI: 10.1007/s44311-025-00022-8
Amine Abbad-Andaloussi, Marco Franceschetti, Hugo A López, Clemens Schreiber, Barbara Weber
Ambiguity in business process models can result in multiple interpretations by model readers. This leads to undesirable outcomes such as misunderstandings, unclear allocation of responsibilities, and unexpected behaviors. Despite these potential consequences, the impact of ambiguity on model readers has received limited attention so far. This article presents an eye-tracking study designed to investigate the effects of various types of ambiguity (i.e., layout, semantic, syntactic, and lexical) on readers' cognitive load, comprehension, and visual associations while interpreting process models. In addition, the study delves into the behaviors of model readers when resolving ambiguity in process models. These behaviors are investigated following a qualitative approach combining both eye-tracking and think-aloud data. The results demonstrate that ambiguities significantly influence cognitive load, comprehension, and visual associations, emphasizing the negative effects of ambiguity. Moreover, the qualitative insights suggest that participants exhibit specific behaviors when trying to resolve ambiguities. These findings underscore the need for advanced mechanisms to detect and mitigate ambiguity in process models.
{"title":"When a model gives you mixed signals: cognitive effects and visual behavior.","authors":"Amine Abbad-Andaloussi, Marco Franceschetti, Hugo A López, Clemens Schreiber, Barbara Weber","doi":"10.1007/s44311-025-00022-8","DOIUrl":"https://doi.org/10.1007/s44311-025-00022-8","url":null,"abstract":"<p><p>Ambiguity in business process models can result in multiple interpretations by model readers. This leads to undesirable outcomes such as misunderstandings, unclear allocation of responsibilities, and unexpected behaviors. Despite these potential consequences, the impact of ambiguity on model readers has received limited attention so far. This article presents an eye-tracking study designed to investigate the effects of various types of ambiguity (i.e., layout, semantic, syntactic, and lexical) on readers' cognitive load, comprehension, and visual associations while interpreting process models. In addition, the study delves into the behaviors of model readers when resolving ambiguity in process models. These behaviors are investigated following a qualitative approach combining both eye-tracking and think-aloud data. The results demonstrate that ambiguities significantly influence cognitive load, comprehension, and visual associations, emphasizing the negative effects of ambiguity. Moreover, the qualitative insights suggest that participants exhibit specific behaviors when trying to resolve ambiguities. These findings underscore the need for advanced mechanisms to detect and mitigate ambiguity in process models.</p>","PeriodicalId":520481,"journal":{"name":"Process science","volume":"2 1","pages":"19"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12500769/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145254294","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}
Pub Date : 2025-01-01Epub Date: 2025-03-20DOI: 10.1007/s44311-025-00006-8
Dominique Sommers, Natalia Sidorova, Boudewijn van Dongen
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.
{"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":"<p><p>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.</p>","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}
扫码关注我们
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号