Cover Image

A Divide-and-Conquer Approach for Modeling Arrival Times in Business Process Simulation

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Kirchdorfer, Lukas, Özdemir, Konrad, Kusenic, Stjepan, van der Aa, Han, Stuckenschmidt, Heiner
Format:	Preprint
Published:	2025
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Rethinking BPS: A Utility-Based Evaluation Framework
by: Özdemir, Konrad, et al.
Published: (2025)

PGTNet: A Process Graph Transformer Network for Remaining Time Prediction of Business Process Instances
by: Elyasi, Keyvan Amiri, et al.
Published: (2024)

AgentSimulator: An Agent-based Approach for Data-driven Business Process Simulation
by: Kirchdorfer, Lukas, et al.
Published: (2024)

Enabling Mixed Effects Neural Networks for Diverse, Clustered Data Using Monte Carlo Methods
by: Tschalzev, Andrej, et al.
Published: (2024)

Analytical Uncertainty-Based Loss Weighting in Multi-Task Learning
by: Kirchdorfer, Lukas, et al.
Published: (2024)

CountTRuCoLa: Rule Confidence Learning for Temporal Knowledge Graph Forecasting
by: Gastinger, Julia, et al.
Published: (2025)

On the Simplification of Neural Network Architectures for Predictive Process Monitoring
by: Ansari, Amaan, et al.
Published: (2025)

Reevaluation of Inductive Link Prediction
by: Ott, Simon, et al.
Published: (2024)

GRANDE: Gradient-Based Decision Tree Ensembles for Tabular Data
by: Marton, Sascha, et al.
Published: (2023)

Diffusion Generative Modelling for Divide-and-Conquer MCMC
by: Trojan, C., et al.
Published: (2024)

A Data-Centric Perspective on Evaluating Machine Learning Models for Tabular Data
by: Tschalzev, Andrej, et al.
Published: (2024)

GradTree: Learning Axis-Aligned Decision Trees with Gradient Descent
by: Marton, Sascha, et al.
Published: (2023)

PLM-eXplain: Divide and Conquer the Protein Embedding Space
by: van Eck, Jan, et al.
Published: (2025)

Divide, Reweight, and Conquer: A Logit Arithmetic Approach for In-Context Learning
by: Huang, Chengsong, et al.
Published: (2024)

Divide, Conquer, Combine Bayesian Decision Tree Sampling
by: Cochrane, Jodie A., et al.
Published: (2024)

Robust and Explainable Divide-and-Conquer Learning for Intrusion Detection
by: Zhou, Yan, et al.
Published: (2026)

Divide-and-Conquer Posterior Sampling for Denoising Diffusion Priors
by: Janati, Yazid, et al.
Published: (2024)

Online Discovery of Simulation Models for Evolving Business Processes (Extended Version)
by: Vinci, Francesco, et al.
Published: (2025)

History repeats Itself: A Baseline for Temporal Knowledge Graph Forecasting
by: Gastinger, Julia, et al.
Published: (2024)

Toward Effective Multimodal Graph Foundation Model: A Divide-and-Conquer Based Approach
by: Liu, Sicheng, et al.
Published: (2026)

Explaining Neural Networks without Access to Training Data
by: Marton, Sascha, et al.
Published: (2022)

Accurate and Scalable Matrix Mechanisms via Divide and Conquer
by: He, Guanlin, et al.
Published: (2026)

From Global Policies to Local Strategies: Multi-Objective Optimization of Resource-Specific Handover Policies
by: Kirchdorfer, Lukas, et al.
Published: (2026)

Unreflected Use of Tabular Data Repositories Can Undermine Research Quality
by: Tschalzev, Andrej, et al.
Published: (2025)

Mitigating Information Loss in Tree-Based Reinforcement Learning via Direct Optimization
by: Marton, Sascha, et al.
Published: (2024)

Transitive RL: Value Learning via Divide and Conquer
by: Park, Seohong, et al.
Published: (2025)

Recursive Decomposition with Dependencies for Generic Divide-and-Conquer Reasoning
by: Hernández-Gutiérrez, Sergio, et al.
Published: (2025)

An Examination on the Effectiveness of Divide-and-Conquer Prompting in Large Language Models
by: Zhang, Yizhou, et al.
Published: (2024)

Divide, Harmonize, Then Conquer It: Shooting Multi-Commodity Flow Problems with Multimodal Language Models
by: Yuan, Xinyu, et al.
Published: (2026)

Dynamic Dual Buffer with Divide-and-Conquer Strategy for Online Continual Learning
by: Dai, Congren, et al.
Published: (2025)

Divide-Fuse-Conquer: Eliciting "Aha Moments" in Multi-Scenario Games
by: Zhang, Xiaoqing, et al.
Published: (2025)

Divide-and-Conquer CoT: RL for Reducing Latency via Parallel Reasoning
by: Mahankali, Arvind, et al.
Published: (2026)

Statistical Optimality of Divide and Conquer Kernel-based Functional Linear Regression
by: Liu, Jiading, et al.
Published: (2022)

Divide and Conquer: Provably Unveiling the Pareto Front with Multi-Objective Reinforcement Learning
by: Röpke, Willem, et al.
Published: (2024)

Prompt, Divide, and Conquer: Bypassing Large Language Model Safety Filters via Segmented and Distributed Prompt Processing
by: Wahréus, Johan, et al.
Published: (2025)

TabPrep: Closing the Feature Engineering Gap in Tabular Benchmarks
by: Tschalzev, Andrej, et al.
Published: (2026)

A Short Note on Batch-efficient Divide-and-Conquer Algorithm for EigenDecomposition
by: Song, Yue
Published: (2026)

Divide-or-Conquer? Which Part Should You Distill Your LLM?
by: Wu, Zhuofeng, et al.
Published: (2024)

FPGA Divide-and-Conquer Placement using Deep Reinforcement Learning
by: Wang, Shang, et al.
Published: (2024)

What is Business Process Automation Anyway?
by: Vu, Hoang, et al.
Published: (2025)