{"title":"Complex-Event Mining Over Centralized and Distributed Data Streams","authors":"M. Garofalakis","doi":"10.1109/icpm57379.2022.9980807","DOIUrl":"https://doi.org/10.1109/icpm57379.2022.9980807","url":null,"abstract":"","PeriodicalId":255646,"journal":{"name":"International Conference on Process Mining","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115633804","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Visual Analytics Meets Process Mining: Challenges and Opportunities","authors":"S. Miksch","doi":"10.1109/icpm53251.2021.9576854","DOIUrl":"https://doi.org/10.1109/icpm53251.2021.9576854","url":null,"abstract":"","PeriodicalId":255646,"journal":{"name":"International Conference on Process Mining","volume":"59 10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121368755","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Keynote Data Mining on Process Data","authors":"T. Seidl","doi":"10.1109/icpm49681.2020.00011","DOIUrl":"https://doi.org/10.1109/icpm49681.2020.00011","url":null,"abstract":"Data Mining and Process Mining – is one just a variant of the other, or do worlds separate the two areas from each other? The notions sound so similar but the contents sometimes look differently, so respective researchers may get confused in their mutual perception, be it authors or reviewers. The talk recalls commonalities like model-based supervised and unsupervised learning approaches, and it also sheds light to peculiarities in process data and process mining tasks as seen from a data mining perspective. When considering trace data from event log files as time series, as sequences, or as activity sets, quite different data mining techniques apply and may be extended and improved. A particular example is rare pattern mining, which fills a gap between frequent patterns and outlier detection. The task aims at identifying patterns that occur with low frequency but above single outliers. Structural deficiences may cause malfunctions or other undesired behavior which get discarded as outliers in event logs, since they are observed infrequently only. Rare pattern mining may identify these situations, and recent approaches include clustering or ordering non-conformant traces.The talk concludes with some remarks on how to sell process mining papers to the data mining community, and vice versa, in order to improve mutual acceptance, and to increase synergies in the fields.","PeriodicalId":255646,"journal":{"name":"International Conference on Process Mining","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123591933","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Scalable Mixed-Paradigm Trace Clustering using Super-Instances","authors":"P. D. Koninck, Jochen De Weerdt","doi":"10.1109/ICPM.2019.00014","DOIUrl":"https://doi.org/10.1109/ICPM.2019.00014","url":null,"abstract":"In process mining, one is often confronted with datasets that contain high degrees of control-flow variation. This causes the results of subsequent process mining steps to be less accurate or harder to understand. Trace clustering, or dividing the log into more homogeneous groups, can help limit this issue. Trace clustering techniques are commonly subdivided into two types: distance-based techniques and model-driven approaches. In this paper, a novel trace clustering technique is presented, that combines aspects of both paradigms. The core idea is to first learn so-called super-instances using a simple distance-driven technique and subsequently apply a model-driven technique to cluster the super-instances. Our technique not only shows qualitative improvements of the obtained clusterings, but also significantly improves the scalability, as shown in an experimental evaluation using real-life event logs.","PeriodicalId":255646,"journal":{"name":"International Conference on Process Mining","volume":"246 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126046921","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}