Efficient Distance Pruning for Process Suffix Comparison in Prescriptive Process Monitoring

TL;DR

This paper introduces a triangle inequality-based pruning method that significantly accelerates process suffix comparison in prescriptive process monitoring without sacrificing accuracy, enabling scalable and efficient decision support.

Contribution

It presents an exact, parallelizable pruning technique leveraging optimized pivots to reduce computational costs in suffix comparison tasks.

Findings

Runtime reduced significantly with pruning

Pruning maintains comparison accuracy

Method is fully parallelizable

Abstract

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.