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"Gelobtes Land". Der europäische Traum und die Bedeutung von Religion/en in pluralen Gesellschaften und Migrationskontexten
Get PDFAuswirkungen des MoPeG auf die Unternehmensnachfolge in Personengesellschaften : Anteilsübertragung, -vererbung, Testamentsvollstreckung
No full textMuch ado about nothing? Understanding Germany’s Bündnis Sahra Wagenknecht (BSW) potential voters
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Enhancing the quality and reproducibility of research: Preferred evaluation of cognitive and neuropsychological studies - The PECANS statement for human studies
Get PDFMapping left-right associations: a framework using open-ended survey responses and political positions
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Framing supply chain sustainability misconducts: The influence of social and environmental framing on buyers’ purchase intentions and attitudes
No full textA simple and calibrated approach for uncertainty-aware remaining time prediction
No full textBusiness processes are typically supported by information systems that log execution data, enabling the prediction of remaining time for ongoing process instances. Deep learning models are often used for this task due to their accuracy, but they only provide point estimates, without accounting for uncertainty. This limits their reliability, as decision-making often benefits from prediction intervals. Uncertainty quantification techniques can help by estimating both expected values and uncertainty. However, existing techniques are often poorly calibrated, computationally expensive, or not adaptable to different deep learning models. This paper examines these challenges and proposes a simple, efficient solution using Laplace approximation and calibrated regression. Our approach distinguishes between model and data uncertainty, integrates easily with any deep learning model, and can be applied to pre-trained networks. Benchmarking on 10 real-world event logs shows that our method matches state-of-the-art performance while significantly reducing training and inference time. This makes it a strong yet simple baseline for uncertainty-aware remaining time prediction in business processes