Advanced Information Systems Engineering [electronic resource] : 29th International Conference, CAiSE 2017, Essen, Germany, June 12-16, 2017, Proceedings / edited by Eric Dubois, Klaus Pohl.
Contributor(s): Dubois, Eric [editor.] | Pohl, Klaus [editor.] | SpringerLink (Online service).
Material type: BookSeries: Information Systems and Applications, incl. Internet/Web, and HCI: 10253Publisher: Cham : Springer International Publishing : Imprint: Springer, 2017Edition: 1st ed. 2017.Description: XXI, 650 p. 215 illus. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783319595368.Subject(s): Application software | Software engineering | Database management | Artificial intelligence | Information technology -- Management | Computer and Information Systems Applications | Software Engineering | Database Management | Artificial Intelligence | Computer Application in Administrative Data ProcessingAdditional physical formats: Printed edition:: No title; Printed edition:: No titleDDC classification: 005.3 Online resources: Click here to access onlineInformation 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.
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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