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Titlebook: Business Process Management; 13th International C Hamid Reza Motahari-Nezhad,Jan Recker,Matthias Wei Conference proceedings 2015 Springer I

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發(fā)表于 2025-3-21 19:58:09 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
期刊全稱Business Process Management
期刊簡稱13th International C
影響因子2023Hamid Reza Motahari-Nezhad,Jan Recker,Matthias Wei
視頻videohttp://file.papertrans.cn/193/192318/192318.mp4
學(xué)科分類Lecture Notes in Computer Science
圖書封面Titlebook: Business Process Management; 13th International C Hamid Reza Motahari-Nezhad,Jan Recker,Matthias Wei Conference proceedings 2015 Springer I
影響因子This book constitutes the proceedings of the 13th International Conference on Business Process Management, BPM 2015, held in Innsbruck, Austria, in August/September 2015. .The 21 regular papers, 7 short papers and 2 inductrial papers included in this volume were carefully reviewed and selected from 125 submissions. The papers are organized in topical sections on runtime process management, process modeling, process modeling discovery, business process models and analytics, BPM in industry, process compliance and deviations, energing and practical areas of BPM, and process monitoring.
Pindex Conference proceedings 2015
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書目名稱Business Process Management影響因子(影響力)




書目名稱Business Process Management影響因子(影響力)學(xué)科排名




書目名稱Business Process Management網(wǎng)絡(luò)公開度




書目名稱Business Process Management網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Business Process Management被引頻次




書目名稱Business Process Management被引頻次學(xué)科排名




書目名稱Business Process Management年度引用




書目名稱Business Process Management年度引用學(xué)科排名




書目名稱Business Process Management讀者反饋




書目名稱Business Process Management讀者反饋學(xué)科排名




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0302-9743 usiness process models and analytics, BPM in industry, process compliance and deviations, energing and practical areas of BPM, and process monitoring.978-3-319-23062-7978-3-319-23063-4Series ISSN 0302-9743 Series E-ISSN 1611-3349
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An Extended Role for the Rural Physician,rds ILP-based process discovery. The technique helps to produce process models that are less over-fitting w.r.t. the event log, more understandable, and more adequate in capturing the dominant behavior present in the event log. The technique is implemented in the ProM framework.
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Avoiding Over-Fitting in ILP-Based Process Discoveryrds ILP-based process discovery. The technique helps to produce process models that are less over-fitting w.r.t. the event log, more understandable, and more adequate in capturing the dominant behavior present in the event log. The technique is implemented in the ProM framework.
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