Prescriptive process monitoring seeks to recommend actions that improve process outcomes by analyzing possible continuations of ongoing cases. A key obstacle is the heavy computational cost of large-scale suffix comparisons, which grows rapidly with log size. We propose an efficient retrieval method exploiting the triangle inequality: distances to a set of optimized pivots define bounds that prune redundant comparisons. This substantially reduces runtime and is fully parallelizable. Crucially, pruning is exact: the retrieved suffixes are identical to those from exhaustive comparison, thereby preserving accuracy. These results show that metric-based pruning can accelerate suffix comparison and support scalable prescriptive systems.

翻译：规范性过程监控旨在通过分析进行中案例的可能延续路径，推荐能够改善过程结果的行动。其面临的主要障碍是大规模后缀比较带来的沉重计算成本，该成本随日志规模迅速增长。本文提出一种利用三角不等式的高效检索方法：通过计算到一组优化枢轴点的距离来界定边界，从而剪除冗余比较。该方法显著降低了运行时间，且具备完全可并行化的特性。关键优势在于剪枝是精确的：检索得到的后缀与穷举比较的结果完全一致，从而保证了准确性。这些结果表明，基于度量的剪枝技术能够有效加速后缀比较，并为可扩展的规范性过程监控系统提供支持。