Data-Driven Process Discovery and Analysis (häftad)

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Format: Häftad (Paperback / softback)
Språk: Engelska
Antal sidor: 185
Utgivningsdatum: 2017-01-28
Upplaga: 1st ed. 2017
Förlag: Springer International Publishing AG
Medarbetare: Ceravolo, Paolo (ed.), Rinderle-Ma, Stefanie (ed.)
Illustratör/Fotograf: Bibliographie 78 schwarz-weiße Abbildungen
Illustrationer: 78 Illustrations, black and white; IX, 185 p. 78 illus.
Dimensioner: 234 x 156 x 11 mm
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Antal komponenter: 1
Komponenter: 1 Paperback / softback
ISBN: 9783319534343

Data-Driven Process Discovery and Analysis

Name: Data-Driven Process Discovery and Analysis
Price: 760.00 SEK
Availability: InStock
Author: Paolo Ceravolo, Stefanie Rinderle-Ma
ISBN: 9783319534343

5th IFIP WG 2.6 International Symposium, SIMPDA 2015, Vienna, Austria, December 9-11, 2015, Revised Selected Papers

av Paolo Ceravolo, Stefanie Rinderle-Ma

Häftad, Engelska, 2017-01-28

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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.

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A Framework for Safety-critical Process Management in Engineering Projects.- Business Process Reporting Using Process Mining, Analytic Workflows and Process Cubes: A Case Study in Education.- Detecting Changes in Process Behavior Using Comparative Case Clustering.- - Using Domain Knowledge to Enhance Process Mining Results.- - Aligning Process Model Terminology with Hypernym Relations.- Time Series Petri Net Models: Enrichment and Prediction.- Visual Analytics Meets Process Mining: Challenges and Opportunities.- A Relational Data Warehouse for Multidimensional Process Mining.