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170608 ||| eng |
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|a 9783319595368
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|a Dubois, Eric
|e [editor]
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|a Advanced Information Systems Engineering
|h Elektronische Ressource
|b 29th International Conference, CAiSE 2017, Essen, Germany, June 12-16, 2017, Proceedings
|c edited by Eric Dubois, Klaus Pohl
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250 |
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|a 1st ed. 2017
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260 |
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|a Cham
|b Springer International Publishing
|c 2017, 2017
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300 |
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|a XXI, 650 p. 215 illus
|b online resource
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|a Information systems architecture -- Business process alignment -- User knowledge discovery -- Business process performance -- Big data exploration -- Process variability management -- Information systems transformation and evolution -- Business process modeling readability -- Business process adaption -- Data mining -- Process discovery -- Business process modeling notation
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653 |
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|a Software engineering
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653 |
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|a Artificial Intelligence
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653 |
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|a Software Engineering
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653 |
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|a Database Management
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653 |
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|a Application software
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653 |
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|a Computer Application in Administrative Data Processing
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653 |
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|a Artificial intelligence
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653 |
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|a Computer and Information Systems Applications
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653 |
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|a Information technology / Management
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653 |
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|a Database management
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700 |
1 |
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|a Pohl, Klaus
|e [editor]
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0 |
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|a eng
|2 ISO 639-2
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989 |
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|b Springer
|a Springer eBooks 2005-
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|a Information Systems and Applications, incl. Internet/Web, and HCI
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5 |
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|a 10.1007/978-3-319-59536-8
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856 |
4 |
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|u https://doi.org/10.1007/978-3-319-59536-8?nosfx=y
|x Verlag
|3 Volltext
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|a 005.3
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|a This book constitutes the refereed proceedings of the 29th International Conference on Advanced Information Systems Engineering, CAiSE 2017, held in Essen, Germany, in June 2017. The 37 papers presented together with 3 keynote papers in this volume were carefully reviewed and selected from 175 submissions. The papers are organized in topical sections on information systems architecture; business process alignment; user knowledge discovery; business process performance; big data exploration; process variability management; information systems transformation and evolution; business process modeling readability; business process adaption; data mining; process discovery; business process modeling notation
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