# Behavioral Petri Net Mining and Automated Analysis for Human-Computer Interaction Recommendations in Multi-Application Environments

## TL;DR

This paper introduces a method using Behavioral Petri Nets to analyze user interactions in multi-application environments, providing recommendations to improve efficiency and adapt behavior towards optimal strategies based on logged user data.

## Contribution

It presents a novel approach applying Petri Net-based process mining to HCI, enabling detection of inefficient behaviors and offering automated recommendations for user interaction improvement.

## Key findings

- Effective detection of inefficient user behaviors.
- Successful application in Windows environment with simulated data.
- Potential to enhance user interaction efficiency.

## Abstract

Process Mining is a famous technique which is frequently applied to Software Development Processes, while being neglected in Human-Computer Interaction (HCI) recommendation applications. Organizations usually train employees to interact with required IT systems. Often, employees, or users in general, develop their own strategies for solving repetitive tasks and processes. However, organizations find it hard to detect whether employees interact efficiently with IT systems or not. Hence, we have developed a method which detects inefficient behavior assuming that at least one optimal HCI strategy is known. This method provides recommendations to gradually adapt users' behavior towards the optimal way of interaction considering satisfaction of users. Based on users' behavior logs tracked by a Java application suitable for multi-application and multi-instance environments, we demonstrate the applicability for a specific task in a common Windows environment utilizing realistic simulated behaviors of users.