ProReco: A Process Discovery Recommender System

TL;DR

ProReco is a recommender system that suggests the most suitable process discovery algorithm for a given event log, considering user preferences and log features, and provides explanations using eXplainable AI techniques.

Contribution

It introduces ProReco, which extends feature pools, incorporates multiple algorithms, and applies XAI to improve process discovery algorithm selection.

Findings

ProReco effectively recommends algorithms tailored to user needs.

The system enhances decision-making with explainable AI insights.

It outperforms baseline methods in accuracy of recommendations.

Abstract

Process discovery aims to automatically derive process models from historical execution data (event logs). While various process discovery algorithms have been proposed in the last 25 years, there is no consensus on a dominating discovery algorithm. Selecting the most suitable discovery algorithm remains a challenge due to competing quality measures and diverse user requirements. Manually selecting the most suitable process discovery algorithm from a range of options for a given event log is a time-consuming and error-prone task. This paper introduces ProReco, a Process discovery Recommender system designed to recommend the most appropriate algorithm based on user preferences and event log characteristics. ProReco incorporates state-of-the-art discovery algorithms, extends the feature pools from previous work, and utilizes eXplainable AI (XAI) techniques to provide explanations for its recommendations.