Titlebook: Business Process Management Workshops; BPM 2023 Internation Jochen De Weerdt,Luise Pufahl Conference proceedings 2024 The Editor(s) (if app

intrinsic

书目名称Business Process Management Workshops影响因子(影响力)




书目名称Business Process Management Workshops影响因子(影响力)学科排名




书目名称Business Process Management Workshops网络公开度




书目名称Business Process Management Workshops网络公开度学科排名




书目名称Business Process Management Workshops被引频次




书目名称Business Process Management Workshops被引频次学科排名




书目名称Business Process Management Workshops年度引用




书目名称Business Process Management Workshops年度引用学科排名




书目名称Business Process Management Workshops读者反馈




书目名称Business Process Management Workshops读者反馈学科排名

leniency

climax

Medienanalyse als Beobachtung und als Kritik view their operational processes from any perspective using a single source of truth. However, OCED is not suitable for low-level machine data that contain a mixture of continuous measurements (e.g., time series data describing position, temperature, force, speed, etc.) and discrete events. Therefo

Herpetologist

1865-1348 Processing for Business Process Management (NLP4BPM 2023).• 1st International Workshop on Object-Centric Processes from A to Z (OBJECTS 2023).• 3rd Internation978-3-031-50973-5978-3-031-50974-2Series ISSN 1865-1348 Series E-ISSN 1865-1356

Implicit

现晕光

An Experiment on Transfer Learning for Suffix Prediction on Event Logs two sequential deep learning architectures (GPT and LSTM). Base models are trained on two public event logs and used as starting point for transfer learning on eight event logs from different domains. The experiments show that even with half of the available training budget and without using very l

高度赞扬

ProtoNER: Few Shot Incremental Learning for Named Entity Recognition Using Prototypical Networkse need to retain original training dataset for longer duration as well as data re-annotation which is very time consuming task, (2) No intermediate synthetic data generation which tends to add noise and results in model’s performance degradation, and (3) Hybrid loss function which allows model to re

持久

新奇

https://doi.org/10.1007/978-3-031-50974-2business process management; business process modeling; business process monitoring; business process o

BLA