Requirements
• Basic knowledge of programming language Python
• Basic knowledge of issues Data Science
Audience
• Specialists Data Science
• Developers Python interested in expanding their knowledge of methods for automatic process discovery and gaining process insight from data
Description
Extending Process Mining analytics using tools in the Python language allows for great flexibility when deeper insight into process data is required. Process Mining itself (pl. Process mining) is a technique that applies algorithms to event logs to analyze business processes. Process mining connects data to processes and provides insight into trends and patterns affecting process performance.
Training plan
Introduction
Process Mining Overview
• Examples of analyses
• Types of notations used in Process Mining
• Data (Event Logs)
• XES data standard
Process Mining in Python
• PM4Py library
• Data structures for processes
• Process discovery algorithms (alpha algorithm, alpha+, …)
Exercises
• ETL (Extract, Transform, Load) for Process Mining
• Directly-Follows Graphs
• Inductive Process Mining
• Visualization of process models
• Analyzes visualization
• Process model metrics - confusion matrix, fitness and precision
• Compliance testing
• Sojourn time vs waiting time
• bottlenecks
Summary and Conclusions