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Titlebook: Data-Driven Process Discovery and Analysis; 5th IFIP WG 2.6 Inte Paolo Ceravolo,Stefanie Rinderle-Ma Conference proceedings 2017 IFIP Inter

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發(fā)表于 2025-3-21 17:07:39 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書(shū)目名稱(chēng)Data-Driven Process Discovery and Analysis
副標(biāo)題5th IFIP WG 2.6 Inte
編輯Paolo Ceravolo,Stefanie Rinderle-Ma
視頻videohttp://file.papertrans.cn/264/263314/263314.mp4
概述Includes supplementary material:
叢書(shū)名稱(chēng)Lecture Notes in Business Information Processing
圖書(shū)封面Titlebook: Data-Driven Process Discovery and Analysis; 5th IFIP WG 2.6 Inte Paolo Ceravolo,Stefanie Rinderle-Ma Conference proceedings 2017 IFIP Inter
描述.This book constitutes the revised selected papers from the 5th IFIP WG 2.6 International Symposium? on Data-Driven Process Discovery and Analysis, SIMPDA 2015, held in Vienna, Austria in December 2015.?.The 8 papers presented in this volume were carefully reviewed and selected from 22 submissions. They cover theoretical issues related to process representation, discovery and analysis, or provide practical and operational experiences in process discovery and analysis. They focus mainly on the adoption of process mining algorithms in conjunction and coordination with other techniques and methodologies..?
出版日期Conference proceedings 2017
關(guān)鍵詞BPM; Business Process Management; Data Mining; Process Analysis; Process Mining
版次1
doihttps://doi.org/10.1007/978-3-319-53435-0
isbn_softcover978-3-319-53434-3
isbn_ebook978-3-319-53435-0Series ISSN 1865-1348 Series E-ISSN 1865-1356
issn_series 1865-1348
copyrightIFIP International Federation for Information Processing 2017
The information of publication is updating

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Business Process Reporting Using Process Mining, Analytic Workflows and Process Cubes: A Case Studyes that allow businesses to understand and improve their processes. One of the most common applications of BPI is ., which consists on the structured generation of information (i.e., reports) from raw data. In this article, state-of-the-art process mining techniques are used to periodically produce
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Detecting Changes in Process Behavior Using Comparative Case Clustering,ntinuously evolve over time. For example, in the healthcare domain, advances in medicine trigger changes in diagnoses and treatment processes. Case data (e.g. treating physician, patient age) also influence how processes are executed. Existing . techniques assume processes to be static and therefore
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Using Domain Knowledge to Enhance Process Mining Results,ithout allowing the domain expert to influence the discovery in any way. However, the user may have certain domain expertise which should be exploited to create better process models. In this paper, we address this issue of incorporating domain knowledge to improve the discovered process model. Firs
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A Relational Data Warehouse for Multidimensional Process Mining,ibutes. For each sublog, a separated process model is discovered and compared to other models to identify group-specific differences for the process. For an effective explorative process analysis, performance is vital due to the explorative characteristics of the analysis. We propose to adopt well-e
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