并发感知的基于因果网的过程模型预测
Concurrency-Aware Process Model Forecasting with Causal Nets
- KU Leuven（鲁汶大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本研究将过程模型预测扩展到因果网，通过预测关系与绑定计数重构未来模型，实验显示其一致性接近重挖掘模型且优于静态基线。
AI中文摘要：
过程模型预测（PMF）旨在预测表征未来时期的过程模型，从而提供行为预期如何演化的过程级视图。然而，现有的PMF方法预测的是直接跟随图，无法显式表示并发性。我们将PMF扩展到因果网，通过预测关系和绑定计数的时间序列，并利用这些预测重构具有AND/XOR语义的未来过程模型。为了评估所得模型，我们引入了一种协议，该协议考虑部分轨迹并构建用于一致性检查的工作流网。在四个事件日志上的实验表明，预测模型达到的一致性水平接近从相应未来窗口中的观测重新挖掘的模型。它们也优于静态发现基线，后者在结构稳定的日志上保持高精度，但在其他三个日志上表现出显著的精度损失。过滤不频繁的绑定改善大多数一致性指标，尽管它也移除了模型捕获的许多并发行为。
英文摘要：
Process model forecasting (PMF) aims to predict the process model that will characterize a future period, thereby providing a process-level view of how behavior is expected to evolve. Existing PMF methods, however, forecast directly-follows graphs, which cannot explicitly represent concurrency. We extend PMF to causal nets by forecasting time series of relation and binding counts and using these forecasts to reconstruct future process models with AND/XOR semantics. To evaluate the resulting models, we introduce a protocol that accounts for partial traces and constructs the workflow nets required for conformance checking. Experiments on four event logs show that the forecasted models achieve conformance levels close to those of models re-mined from observations in the corresponding future windows. They also outperform static discovery baselines, which retain high precision on the structurally stable log but exhibit substantial precision losses on the other three logs. Filtering infrequent bindings improves most conformance metrics, although it also removes much of the concurrent behavior captured by the models.
