# Efficient Distance Pruning for Process Suffix Comparison in Prescriptive Process Monitoring

arXiv: https://arxiv.org/abs/2602.09039 · HTML: https://mlsanity.com/paper/2602.09039/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 4 references that are in the map.
- First posted on arXiv 30 Jan 2026.

## Builds on (most cited first, 1 of 1)

- [Increasing Scalability of Process Mining using Event Dataframes: How Data Structure Matters](https://mlsanity.com/paper/1907.12817/) (2019)

## Abstract (arXiv)

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.