Ranking the Top-K Realizations of Stochastically Known Event Logs

TL;DR

This paper introduces an efficient algorithm for ranking the top-K most probable realizations of stochastically known event logs, improving uncertainty handling in process mining by considering multiple likely event sequences.

Contribution

The paper presents a linear-time algorithm for top-K realization ranking under event independence, enabling more effective uncertainty-aware process analysis.

Findings

Top-K ranking improves over top-1 in uncertain event logs.

Algorithm operates in O(Kn) time, scalable to larger logs.

Top-K benefits depend on log length and probability distribution.

Abstract

Various kinds of uncertainty can occur in event logs, e.g., due to flawed recording, data quality issues, or the use of probabilistic models for activity recognition. Stochastically known event logs make these uncertainties transparent by encoding multiple possible realizations for events. However, the number of realizations encoded by a stochastically known log grows exponentially with its size, making exhaustive exploration infeasible even for moderately sized event logs. Thus, considering only the top-K most probable realizations has been proposed in the literature. In this paper, we implement an efficient algorithm to calculate a top-K realization ranking of an event log under event independence within O(Kn), where n is the number of uncertain events in the log. This algorithm is used to investigate the benefit of top-K rankings over top-1 interpretations of stochastically known event logs. Specifically, we analyze the usefulness of top-K rankings against different properties of the input data. We show that the benefit of a top-K ranking depends on the length of the input event log and the distribution of the event probabilities. The results highlight the potential of top-K rankings to enhance uncertainty-aware process mining techniques.