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Record Nr. |
UNINA9910437602903321 |
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Titolo |
Data-Driven Process Discovery and Analysis : Second IFIP WG 2.6, 2.12 International Symposium, SIMPDA 2012, Campione d'Italia, Italy, June 18-20, 2012, Revised Selected Papers / / edited by Philippe Cudré-Mauroux, Paolo Ceravolo, Dragan Gaševic |
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Pubbl/distr/stampa |
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Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2013 |
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ISBN |
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Edizione |
[1st ed. 2013.] |
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Descrizione fisica |
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1 online resource (X, 113 p. 41 illus.) |
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Collana |
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Lecture Notes in Business Information Processing, , 1865-1356 ; ; 162 |
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Disciplina |
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Soggetti |
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Application software |
Business information services |
Data mining |
Computer and Information Systems Applications |
IT in Business |
Data Mining and Knowledge Discovery |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Bibliographic Level Mode of Issuance: Monograph |
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Nota di contenuto |
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A Lightweight RDF Data Model for Business Process Analysis -- Combination of Process Mining and Simulation Techniques for Business Process Redesign: A Methodological Approach -- Improving Business Process Models Using Observed Behavior -- Process Prediction in Noisy Data Sets: A Case Study in a Dutch Hospital -- Towards Automatic Capturing of Semi-structured Process Provenance -- Managing Structural and Textual Quality of Business Process Models. |
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Sommario/riassunto |
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This book constitutes the thoroughly refereed proceedings of the Second International Symposium on Data-Driven Process Discovery and Analysis held in Campione d'Italia, Italy, in June 2012. The six revised full papers were carefully selected from 17 submissions. To improve the quality of the contributions the symposium fostered the discussion during the presentation, giving authors the opportunity to improve their work extending the presented results. The selected papers cover topics spanning from theoretical issues related to process |
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representation, discovery and analysis to practical and operational experiences in process discovery and analysis. |
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