Discovering Process Models from Uncertain Event Data

TL;DR

This paper introduces a method for discovering process models from uncertain event logs, incorporating explicit uncertainty information to improve process analysis accuracy.

Contribution

It presents a novel technique to derive directly-follows graphs from uncertain data and applies inductive mining to capture both certain and uncertain process aspects.

Findings

Effective directly-follows graph construction from uncertain logs

Successful application of inductive mining on uncertain data

Models representing both certain and uncertain process parts

Abstract

Modern information systems are able to collect event data in the form of event logs. Process mining techniques allow to discover a model from event data, to check the conformance of an event log against a reference model, and to perform further process-centric analyses. In this paper, we consider uncertain event logs, where data is recorded together with explicit uncertainty information. We describe a technique to discover a directly-follows graph from such event data which retains information about the uncertainty in the process. We then present experimental results of performing inductive mining over the directly-follows graph to obtain models representing the certain and uncertain part of the process.