2020 2nd International Conference on Process Mining (ICPM)最新文献_第3页

Process Mining Meets Causal Machine Learning: Discovering Causal Rules from Event Logs 过程挖掘与因果机器学习:从事件日志中发现因果规则

2020 2nd International Conference on Process Mining (ICPM) Pub Date : 2020-09-03 DOI: 10.1109/ICPM49681.2020.00028

Z. Bozorgi, Irene Teinemaa, M. Dumas, M. Rosa, Artem Polyvyanyy

引用次数: 32

Identifying Candidate Routines for Robotic Process Automation from Unsegmented UI Logs 从未分段的UI日志中识别机器人过程自动化的候选例程

2020 2nd International Conference on Process Mining (ICPM) Pub Date : 2020-08-13 DOI: 10.1109/ICPM49681.2020.00031

Volodymyr Leno, Adriano Augusto, M. Dumas, M. Rosa, F. Maggi, Artem Polyvyanyy

{"title":"Identifying Candidate Routines for Robotic Process Automation from Unsegmented UI Logs","authors":"Volodymyr Leno, Adriano Augusto, M. Dumas, M. Rosa, F. Maggi, Artem Polyvyanyy","doi":"10.1109/ICPM49681.2020.00031","DOIUrl":"https://doi.org/10.1109/ICPM49681.2020.00031","url":null,"abstract":"Robotic Process Automation (RPA) is a technology to develop software bots that automate repetitive sequences of interactions between users and software applications (a.k. a. routines). To take full advantage of this technology, organizations need to identify and to scope their routines. This is a challenging endeavor in large organizations, as routines are usually not concentrated in a handful of processes, but rather scattered across the process landscape. Accordingly, the identification of routines from User Interaction (UI) logs has received significant attention. Existing approaches to this problem assume that the UI log is segmented, meaning that it consists of traces of a task that is presupposed to contain one or more routines. However, a UI log usually takes the form of a single unsegmented sequence of events. This paper presents an approach to discover candidate routines from unsegmented UI logs in the presence of noise, i.e. events within or between routine instances that do not belong to any routine. The approach is implemented as an open-source tool and evaluated using synthetic and real-life UI logs.","PeriodicalId":223427,"journal":{"name":"2020 2nd International Conference on Process Mining (ICPM)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117291387","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}

引用次数: 19

Explainable Predictive Process Monitoring 可解释的预测过程监控

2020 2nd International Conference on Process Mining (ICPM) Pub Date : 2020-08-04 DOI: 10.1109/ICPM49681.2020.00012

Musabir Musabayli, F. Maggi, Williams Rizzi, Chiara Di Francescomarino

引用次数: 44

An Entropic Relevance Measure for Stochastic Conformance Checking in Process Mining 过程挖掘中随机一致性检验的熵相关度量

2020 2nd International Conference on Process Mining (ICPM) Pub Date : 2020-07-18 DOI: 10.1109/ICPM49681.2020.00024

Artem Polyvyanyy, Alistair Moffat, Luciano Garc'ia-Banuelos

{"title":"An Entropic Relevance Measure for Stochastic Conformance Checking in Process Mining","authors":"Artem Polyvyanyy, Alistair Moffat, Luciano Garc'ia-Banuelos","doi":"10.1109/ICPM49681.2020.00024","DOIUrl":"https://doi.org/10.1109/ICPM49681.2020.00024","url":null,"abstract":"Given an event log as a collection of recorded real-world process traces, process mining aims to automatically construct a process model that is both simple and provides a useful explanation of the traces. Conformance checking techniques are then employed to characterize and quantify commonalities and discrepancies between the log’s traces and the candidate models. Recent approaches to conformance checking acknowledge that the elements being compared are inherently stochastic – for example, some traces occur frequently and others infrequently – and seek to incorporate this knowledge in their analyses.Here we present an entropic relevance measure for stochastic conformance checking, computed as the average number of bits required to compress each of the log’s traces, based on the structure and information about relative likelihoods provided by the model. The measure penalizes traces from the event log not captured by the model and traces described by the model but absent in the event log, thus addressing both precision and recall quality criteria at the same time. We further show that entropic relevance is computable in time linear in the size of the log, and provide evaluation outcomes that demonstrate the feasibility of using the new approach in industrial settings.","PeriodicalId":223427,"journal":{"name":"2020 2nd International Conference on Process Mining (ICPM)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127708960","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}

引用次数: 11

Message from the General Chairs 主席致辞

2020 2nd International Conference on Process Mining (ICPM) Pub Date : 2018-10-01 DOI: 10.1109/snams.2018.8554700

A. Frery

引用次数: 0