[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128662-en":3,"doc-seo-128662-105":31,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},128662,962084925782,"Ava Thompson","https://ap-avatar.wpscdn.com/davatar_9964176cb1d06d4a9deccf72a44ae3dc",8,"Research & Report","On the Impact of Low-Quality Activity Labels in Predictive Process Monitoring - submitted version","Event logs underpin process mining by recording digital traces of process steps with case identifiers, activity labels, and timestamps, yet their quality errors can propagate into unreliable insights. This paper examines how errors affecting activity labels influence predictive process monitoring models. Using publicly available and simulated event logs, experiments evaluate effects on next-activity, outcome, and remaining-time prediction, then derive preliminary guidance for designing data preparation pipelines that prioritize cleaning actions under practical trade-offs.","Paper accepted at the ICPM 2024 Workshop“ML4PM – Leveraging Machine learning in Process Mining”  \nsubmitted version  \nOn the Impact of Low-Quality Activity Labels in  \nPredictive Process Monitoring⋆  \nMarco Comuzzi 1 , Sungkyu Kim 1 , Jonghyeon Ko2 , Musa Salamov3 , Cinzia Cappiello3 , and Barbara Pernici3  \n1 Ulsan National Institute of Science and Technology, Ulsan, Korea  \n2 Jeonju University, Jeonju, Korea  \n3 Politecnico di Milano, Milan, Italy  \n{mcomuzzi,[kimkangf3}@unist.ac.kr](kimkangf3}@unist.ac.kr) , [whd1gus2@jj.ac.kr](whd1gus2@jj.ac.kr)[ ](whd1gus2@jj.ac.kr){musa.salamov,cinzia.cappiello,[barbara.pernici}@polimi.it](barbara.pernici}@polimi.it)  \nAbstract. While event log data quality is recognized as a crucial concern in process mining, the impact of event log errors on different types of process mining tasks has remained largely unexplored. This paper aims to fill such a gap by analyzing how various errors affect analysis results.  \nIn particular, we aim to assess whether and to what extent different types of errors that impact the quality of activity labels affect the performance of predictive process monitoring models, considering the three main tasks of next activity, outcome, and remaining time prediction, using publicly available and simulated event logs. The results of the experiments are used to extract preliminary insights into the design of data preparation pipelines for predictive process monitoring.  \nKeywords: data quality · data science pipeline · classification.  \n1 Introduction  \nProcess mining aims to extract insights on business processes using the data in so-called event logs [19] . Event logs collect digital traces of events, capturing the occurrence of process steps. Events may be logged by human actors or information systems used in the execution of the process. For each event, an event log must contain at least an ID of the process execution to which the event belongs, [a.k.a. case](a.k.a. case) ID, a label indicating the activity that the event has recorded, and a timestamp. As a (process) data science and analytics discipline, process mining is subject to the tenet of garbage in, garbage out: the lower the quality of the input event logs, the lower the quality and reliability of the insights that we can extract using process mining [17] .  \n⋆ This work has been supported by the PRIN 2022 Project “Discount quality for responsible data science: Human-in-the-Loop for quality data”, by the PNRR-PE-AI  \n“FAIR” project funded by the NextGenerationEU program, and by the NRF Korea, Grant Number 2022R1F1A1072843 . We thank Federico Toschi from Politecnico di Milano for his support in data profiling and the Apromore Process Mining Academic Alliance for providing log analysis tools.  \n2 M. Comuzzi et al.  \nUnderstanding the effect of errors on the quality of the data analysis results is crucial for designing and improving data science pipelines [3,8] . On the one hand, it can inform the design and configuration of the data-gathering landscape. For instance, information systems and sensors could be configured to avoid practices more likely to lead to high-impact errors during data gathering. On the other hand, it helps designers to prioritize cleaning actions in the input data preparation phase. Data cleaning has a cost, at least in terms of computational time and effort. As such, a trade-off between input data cleaning actions and the expected impact on the data analytics output quality must be found when designing a data science pipeline.  \nWhile several research contributions focus on characterizing event log data quality [17,3], the issue of how low-quality logs impact the quality of process mining results has remained largely unexplored. This paper aims to start a research journey to close this gap. Specifically, as far as errors are concerned, werestrict our attention to the ones affecting the activity labels. This is a fundamental attribute of an event log that is crucial for all process mining","cbCaieFbZVDT1x80","https://ap.wps.com/l/cbCaieFbZVDT1x80","pdf",640347,3,1,12,"English","en",105,"# Introduction\n## Related Work\n## Data Pipeline Design Framework\n## Experiment Design and Results\n## Conclusions","[{\"question\":\"What problem does the paper address?\",\"answer\":\"The paper studies how low-quality activity labels in event logs affect the accuracy of predictive process monitoring tasks.\"},{\"question\":\"Which predictive process monitoring tasks are evaluated?\",\"answer\":\"It evaluates next activity prediction, outcome prediction, and remaining time prediction using predictive process monitoring models.\"},{\"question\":\"How are activity label errors modeled in the study?\",\"answer\":\"The study models imperfections targeting activity labels, including distorted, polluted, homonym, and synonym label patterns.\"}]","On the Impact of Low-Quality Activity Labels in Predictive Process Monitoring - submitted version | PDF",1786002421,30,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":29},"on-the-impact-of-low-quality-activity-labels-in-predictive-process-monitoring-submitted-version","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,51],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":20},"https://docshare.wps.com/document/research-report/",{"item":52,"name":13,"@type":44,"position":53},"https://docshare.wps.com/document/on-the-impact-of-low-quality-activity-labels-in-predictive-process-monitoring-submitted-version/128662/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-25","2026-08-06",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What problem does the paper address?","Question",{"text":76,"@type":77},"The paper studies how low-quality activity labels in event logs affect the accuracy of predictive process monitoring tasks.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Which predictive process monitoring tasks are evaluated?",{"text":81,"@type":77},"It evaluates next activity prediction, outcome prediction, and remaining time prediction using predictive process monitoring models.",{"name":83,"@type":74,"acceptedAnswer":84},"How are activity label errors modeled in the study?",{"text":85,"@type":77},"The study models imperfections targeting activity labels, including distorted, polluted, homonym, and synonym label patterns.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,123,128,131,135],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":48,"doc_module":4,"doc_module_name":47,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":47,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":47,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":47,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":30,"slug":122},"research-report",{"id":124,"doc_module":4,"doc_module_name":47,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":47,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":47,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":47,"category_name":137,"show_sort_weight":107,"slug":138},19,"General","general"]