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Business Process Intelligence Challenge 2017 - Processed

Zenodo2017-02-06 更新2026-04-07 收录

流程挖掘

因果推断

数据链接：

https://zenodo.org/record/5084611 数据链接链接失效反馈

官方服务：

资源简介：

This dataset is the pre-processed version of the BPIC17 dataset. It contains potential confounding features, the treatment indicator, and the cycle time.

应用场景：

创建时间：

2017-02-06

因果推断

观察研究

Data and R code to replicate simulations and empirical applications in the paper.

DataONE2024-02-08 更新130

Healthcare Datasets

医疗数据分析

因果推断

该数据集用于对大型语言模型生成的因果结构进行基准测试，与传统统计方法在医疗数据集上的应用进行比较。此外，该数据集还用于验证大型语言模型生成的贝叶斯网络与专家提取以及统计生成的网络的一致性。该任务旨在进行概率因果建模。

arXiv100

Order Management Object-centric Event Log in OCEL 2.0 Standard

订单管理流程

流程挖掘

General Description This process describes the management of customer orders within a company, comprising both the registration and payment of incoming orders, as well as the process of packing and sh

Mendeley Data2024-06-27 更新60

Process Discovery Contest 2022

流程发现

流程挖掘

This data set contains the data set as was used for the Process Discovery Contest of 2022 (PDC 2022). The data set contains 480 training logs, 96 corresponding test logs, 96 corresponding ground truth

Mendeley Data2024-03-27 更新110

Local Gaussian process extrapolation for BART models with applications to causal inference

回归模型

因果推断

Bayesian additive regression trees (BART) is a semi-parametric regression model offering state-of-the-art performance on out-of-sample prediction. Despite this success, standard implementations of BAR

DataCite Commons2023-07-26 更新90

Business Process Intelligence Challenge 2017 - Processed

资源简介：

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