Titlebook: Business Process Management Workshops; BPM 2013 Internation Niels Lohmann,Minseok Song,Petia Wohed Conference proceedings 2014 Springer Int

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Pander

Digitalisierung und Gesellschaft,malies), handles infrequent behaviour well and finishes quickly. We present a technique able to cope with infrequent behaviour and large event logs, while ensuring soundness. The technique has been implemented in ProM and we compare the technique with existing approaches in terms of quality and performance.

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chronology

photophobia

Discovering Block-Structured Process Models from Event Logs Containing Infrequent Behaviourmalies), handles infrequent behaviour well and finishes quickly. We present a technique able to cope with infrequent behaviour and large event logs, while ensuring soundness. The technique has been implemented in ProM and we compare the technique with existing approaches in terms of quality and performance.

Amylase

Towards a Meta-Model for Goal-Based Social BPMo overcome these limitations. One such idea proposed in this paper is driving BPM enactment by social goals. This paper presents a goal-based approach to social BPM that combines flexibility with the element of collaboration as found in social software.

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Automatic Generation of Business Process Models Based on Attribute Relationship Diagramseration of a BPMN model with decision table schemas for business rule tasks and form attributes for user tasks from the ARD diagram. In our approach, processes and rules are generated simultaneously. Thanks to this, they are complementary and can be directly executed using our hybrid execution environment.

landmark

fructose

Medienvermittelte Kommunikationlity and dynamism that the best methods currently available. This was tested in both simulated and real process event logs. The methods defined in this article may be incorporated into recommendation systems to give a better estimation of process remaining time, allowing them to dynamically learn with each new trace passing through the system.

Melanocytes

Process Remaining Time Prediction Using Query Catalogslity and dynamism that the best methods currently available. This was tested in both simulated and real process event logs. The methods defined in this article may be incorporated into recommendation systems to give a better estimation of process remaining time, allowing them to dynamically learn with each new trace passing through the system.