Titlebook: Business Process Management; 13th International C Hamid Reza Motahari-Nezhad,Jan Recker,Matthias Wei Conference proceedings 2015 Springer I

eucalyptus

現(xiàn)代

Context-Sensitive Textual Recommendations for Incomplete Process Model Elementsto the user. We prototypically implemented our approach and conducted an extensive user experiment with real-world process models in order to assess the usefulness of the recommendations. The results show that our approach fulfills its purpose and creates meaningful recommendations.

FRET

Rohela Mahmud,Yvonne Ai Lian Lim,Amirah Amir scenarios are often generated in a less rigorous way. Only one or a few workshops are organized for this purpose, which rely on the use of techniques that are susceptible to bias and incompleteness, e.g. brainstorming. In this paper, we evaluate a new technique for generating process improvement id

前兆

Detonate

Medical Paratexts from Medieval to Modernrmance analysis do not take both queueing semantics and the process perspective into account. In this work, we address this gap by developing a novel method for utilizing rich process logs to analyze performance of scheduled processes. The proposed method combines simulation, queueing analytics, and

合乎習(xí)俗

輕打

https://doi.org/10.1007/978-3-319-73426-2n the last decade as possible means of addressing the challenge of supporting at the same time flexibility in execution, adaptability and compliance. However, the definition of . semantics, which is a necessary foundation for asynchronously executing distributed processes, is not obvious for formali

內(nèi)疚

genesis

https://doi.org/10.1007/978-3-031-30371-5 in the life-cycle of a process-aware information system. However, in a decade of process discovery research, the relevant algorithms are known to have strong limitations in several dimensions. . and . are two important special structures in a process model. Mining invisible tasks involved in non-fr

清楚說(shuō)話

Medical Practice Variations in Stroke, angles. Most of the contributions consider the extraction of a model as a . problem where only positive information is available. In this paper we present a fresh look at process discovery where also negative information can be taken into account. This feature may be crucial for deriving process mo