Log In Sign Up

Bootstrapping Generalization of Process Models Discovered From Event Data

by   Artem Polyvyanyy, et al.

Process mining studies ways to derive value from process executions recorded in event logs of IT-systems, with process discovery the task of inferring a process model for an event log emitted by some unknown system. One quality criterion for discovered process models is generalization. Generalization seeks to quantify how well the discovered model describes future executions of the system, and is perhaps the least understood quality criterion in process mining. The lack of understanding is primarily a consequence of generalization seeking to measure properties over the entire future behavior of the system, when the only available sample of behavior is that provided by the event log itself. In this paper, we draw inspiration from computational statistics, and employ a bootstrap approach to estimate properties of a population based on a sample. Specifically, we define an estimator of the model's generalization based on the event log it was discovered from, and then use bootstrapping to measure the generalization of the model with respect to the system, and its statistical significance. Experiments demonstrate the feasibility of the approach in industrial settings.


page 1

page 2

page 3

page 4


Generalization in Automated Process Discovery: A Framework based on Event Log Patterns

The importance of quality measures in process mining has increased. One ...

Adversarial System Variant Approximation to Quantify Process Model Generalization

In process mining, process models are extracted from event logs using pr...

Automated simulation and verification of process models discovered by process mining

This paper presents a novel approach for automated analysis of process m...

An Entropic Relevance Measure for Stochastic Conformance Checking in Process Mining

Given an event log as a collection of recorded real-world process traces...

Heuristic Approaches for Generating Local Process Models through Log Projections

Local Process Model (LPM) discovery is focused on the mining of a set of...

Comparing decision mining approaches with regard to the meaningfulness of their results

Decisions and the underlying rules are indispensable for driving process...