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Data-Driven Process Discovery and Analysis : 5th IFIP WG 2.6 International Symposium, SIMPDA 2015, Vienna, Austria, December 9-11, 2015, Revised Selected Papers / edited by Paolo Ceravolo, Stefanie Rinderle-Ma.
- Format:
- Book
- Series:
- Computer Science (Springer-11645)
- Lecture notes in business information processing 1865-1348 ; 244.
- Lecture Notes in Business Information Processing, 1865-1348 ; 244
- Language:
- English
- Subjects (All):
- Data mining.
- Management information systems.
- Industrial management.
- Application software.
- Data Mining and Knowledge Discovery.
- Business Process Management.
- Information Systems Applications (incl. Internet).
- Computer Appl. in Administrative Data Processing.
- Local Subjects:
- Data Mining and Knowledge Discovery.
- Business Process Management.
- Information Systems Applications (incl. Internet).
- Computer Appl. in Administrative Data Processing.
- Physical Description:
- 1 online resource (IX, 185 pages) : 78 illustrations.
- Edition:
- First edition 2017.
- Contained In:
- Springer eBooks
- Place of Publication:
- Cham : Springer International Publishing : Imprint: Springer, 2017.
- System Details:
- text file PDF
- Summary:
- 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. .
- Contents:
- 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. .
- Other Format:
- Printed edition:
- ISBN:
- 978-3-319-53435-0
- 9783319534350
- Access Restriction:
- Restricted for use by site license.
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