将流程合规检查转化为整数规划,显著提升长偏差路径的计算效率。
Developing a Totally Unimodular Linear Program for Optimal Conformance Checking: When and Why It Complements A*

- 把对齐式合规检查重构成可解的线性规划问题
- 在长轨迹上比A*快,平均提速38.6%
- 适合处理长且偏离规范的流程日志
基于对齐的合规检查是对比实际流程执行与规范模型的主流方法。标准精确解法依赖基于A*的启发式搜索,在长轨迹或大偏差情况下可能呈现指数级运行时间。本文将对齐式合规检查重新表述为定义在同步积可达图上的完全幺模线性规划(LP)。利用底层网络流结构,该公式保证了通过LP松弛仍存在整数最优极点解,从而避免整数变量带来的组合开销及分支定界搜索。我们在超过210万条来自真实世界和合成基准数据集的实例上进行了广泛实证评估。结果表明,A*与LP方法性能互补:前者在短而符合规范的轨迹上表现最佳,而后者在长轨迹且含偏差时提供显著加速,而这正是合规检查最需关注的场景。基于此,我们提出了简单算法选择策略,结合两者后平均节省38.6%运行时间,选择准确率达96%,优于始终使用A*。
原文摘要 · Abstract (English)
Alignment-based conformance checking is the state-of-the-art approach for comparing observed process executions with normative process models. The standard exact solution relies on an A*-based heuristic search, which can exhibit exponential runtime in the presence of long traces or substantial deviations. This paper introduces a reformulation of alignment-based conformance checking as a totally unimodular linear program (LP) defined on the reachability graph of the synchronous product. By exploiting the underlying network-flow structure, the proposed formulation guarantees the existence of an integral optimal extreme-point solution through LP relaxation, thereby avoiding the combinatorial overhead associated with integer variables and branch-and-bound search. We conduct an extensive empirical evaluation on more than 2.1 million conformance checking instances derived from real-world and synthetic benchmark datasets. The results show that A* and the LP approach exhibit complementary performance characteristics: the former performs best on short, well-conforming traces, while the LP formulation provides substantial speedups for longer traces with deviations, precisely where conformance checking is most informative. Based on these findings, we derive simple algorithm-selection guidelines that combine both approaches, achieving average runtime savings of 38.6% with 96% selection accuracy compared to always using A*.
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