# Interest-Driven Discovery of Local Process Models

## TL;DR

This paper introduces a goal-driven framework for discovering local process models that focus on business-relevant behavior, enabling process analysts to derive actionable insights from complex event data.

## Contribution

It presents a novel framework using utility functions and constraints for targeted local process model discovery, enhancing usefulness for business process improvement.

## Key findings

- Framework effectively combines multiple utility functions and constraints.
- Application on real datasets yields actionable business insights.
- Approach improves relevance of discovered models for analysts.

## Abstract

Local Process Models (LPM) describe structured fragments of process behavior occurring in the context of less structured business processes. Traditional LPM discovery aims to generate a collection of process models that describe highly frequent behavior, but these models do not always provide useful answers for questions posed by process analysts aiming at business process improvement. We propose a framework for goal-driven LPM discovery, based on utility functions and constraints. We describe four scopes on which these utility functions and constrains can be defined, and show that utility functions and constraints on different scopes can be combined to form composite utility functions/constraints. Finally, we demonstrate the applicability of our approach by presenting several actionable business insights discovered with LPM discovery on two real life data sets.