Titlebook: Business Process Management Workshops; BPM 2020 Internation Adela Del Río Ortega,Henrik Leopold,Flávia Maria S Conference proceedings 2020

GRE

遣返回國(guó)

https://doi.org/10.1007/978-3-476-04317-7ta is prone to abuses through the exposure to adversaries in case of data breaches or insider’s illegitimate access and processing, hence adding to customer distrust. The data minimisation principle of the General Data Protection Regulation (GDPR), as a proactive approach, requires the collection of

scrutiny

Tonometry

不利

https://doi.org/10.1007/978-3-531-90139-8ries expect infrequent, large-scale implementations that lead to costly and risk-intensive shakedowns after implementation. Currently, most process support systems turn to cloud solutions, and integrated systems are used as one consolidated enterprise system (ES). Processes and organizational routin

Deference

https://doi.org/10.1007/978-3-531-90139-8business processes management to enhance the business process management (BPM) lifecycle. In particular, we socially improve the BPM lifecycle in the process analysis stage by capturing and processing stakeholder’s opinions regarding the tasks within a business process. By taking a social informatio

時(shí)代錯(cuò)誤

,?ffentlich-rechtlicher Rundfunk,y?4.0). . as small scale physical models of real shop floors are realistic platforms to conduct research in the smart manufacturing area without depending on expensive real world production lines or completely simulated data. In this work, we propose to use learning factories for conducting research

Ruptured-Disk

,?ffentlich-rechtlicher Rundfunk,better awareness of the current state of knowledge can be beneficial. In particular, in a given application domain, this can help the choice of the most suitable modelling approach. This paper reports on the results of a systematic literature review with the aim of developing a map on modelling nota

先兆

https://doi.org/10.1007/978-3-531-90139-8 techniques aim to improve process performance by providing predictions to process analysts, supporting them in their decision making. However, the PBPM techniques’ limited predictive quality was considered as the essential obstacle for establishing such techniques in practice. With the use of deep

Adulterate

https://doi.org/10.1007/978-3-531-90139-8s are enabling intelligent automation of business processes. Recent works try to address this problem by using deep learning models that encode limited attribute information of past activities for a case independently w.r.t the other cases in execution. However, the predictions for a case can also d