Efficient Time and Space Representation of Uncertain Event Data

TL;DR

This paper introduces an efficient algorithm for constructing graph representations of uncertain event data in process mining, significantly improving performance over previous methods.

Contribution

The paper presents a novel algorithm for behavior graph construction from uncertain process traces, with proven asymptotic complexity and demonstrated order-of-magnitude performance gains.

Findings

Order-of-magnitude faster behavior graph construction

Proven asymptotic time complexity of the algorithm

Experimental results confirm significant performance improvements

Abstract

Process mining is a discipline which concerns the analysis of execution data of operational processes, the extraction of models from event data, the measurement of the conformance between event data and normative models, and the enhancement of all aspects of processes. Most approaches assume that event data is accurately capture behavior. However, this is not realistic in many applications: data can contain uncertainty, generated from errors in recording, imprecise measurements, and other factors. Recently, new methods have been developed to analyze event data containing uncertainty; these techniques prominently rely on representing uncertain event data by means of graph-based models explicitly capturing uncertainty. In this paper, we introduce a new approach to efficiently calculate a graph representation of the behavior contained in an uncertain process trace. We present our novel algorithm, prove its asymptotic time complexity, and show experimental results that highlight order-of-magnitude performance improvements for the behavior graph construction.