Efficient Distance Pruning for Process Suffix Comparison in Prescriptive Process Monitoring
This work addresses scalability issues for practitioners in process monitoring, though it is incremental as it applies known metric-based pruning to a specific domain.
The paper tackles the computational bottleneck of large-scale suffix comparisons in prescriptive process monitoring by proposing an efficient retrieval method using triangle inequality and optimized pivots, which reduces runtime while preserving exact accuracy.
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.