Introduction

In the world of process mining, business rules play a critical role in defining decision logic at process gateways. These rules enable organizations to improve compliance, automate workflows, and make data-driven decisions. However, extracting accurate and interpretable business rules from large datasets is a complex task.

Historically, we relied on decision rules extraction implementation in Java to generate decision trees for business rules mining. As we started supporting larger datasets after the next-gen implementation, we started facing performance and scalability issues. To overcome these challenges, we had to redesign the solution using machine learning techniques in Python.

Background

Our Business Rule Management (BRM) engine analyzes process models (BPMN) and derives business rules for gateways using decision trees. The initial implementation using Java performed adequately for small datasets. However, as data grew, we encountered significant issues:

• Performance Bottlenecks:  Exhibited slow processing speeds when handling datasets larger than 500,000 records.

• High Resource Consumption: CPU and memory utilization increased sharply with data size.

• Scalability Limits: Beyond a certain threshold, additional resources did not yield better performance.

With these constraints, the system reached its performance ceiling, limiting our ability to process large-scale datasets efficiently. We needed a more robust, scalable, and modern approach.

BPMN Diagram

BPMN Gateway

Decision rule

The Solution: Machine Learning-Driven Business Rules Mining

To address these challenges, we migrated from Java to a Python-based machine learning approach using Scikit-learn’s DecisionTreeClassifier. This transition allowed us to adopt advanced preprocessing techniques, improve model interpretability, and scale efficiently to millions of records.

Our enhanced pipeline includes three major stages:

1. Preprocessing for Quality Data

High-quality rules require clean, structured data. Our preprocessing steps included:

• Handling Missing Values

• Removing Redundant Features

  • Applied VarianceThreshold to drop features with zero variance.

• Managing High Cardinality Columns

  • Removed categorical columns with excessively unique values, as they add little value to rule derivation.

• Feature Selection

  • Leveraged SelectKBest to retain the most relevant features for building decision trees.

2. Encoding Strategies

Proper encoding of features ensures that decision trees interpret categorical and numerical data effectively:

• Categorical Features: After testing multiple techniques (Label Encoding, Ordinal Encoding), we finalized One-Hot Encoding, which works well with decision trees.

• Target Variable: Used Target Encoding to map categorical target classes into numerical representations.

3. Model Training and Rule Derivation

For the algorithm, we selected Scikit-learn’s DecisionTreeClassifier with the following configuration:

• Splitting Criterion: Gini Index, chosen for efficient multi-class handling.

• Model Evaluation: Used Confusion Matrix to validate precision, recall, and overall accuracy.

• Rule Extraction: Derived human-readable decision rules by parsing decision paths in the trained tree.

Performance and Scalability: The Results

The impact of this migration was significant. With the same hardware configuration (CPU and memory allocation), the new implementation achieved:

This 10-fold improvement in processing time and the ability to handle 10 million+ records has enabled us to generate rules at scale, supporting complex process models in real-world enterprise environments.

Conclusion

Modernizing our business rules mining engine by transitioning from Java implemetation to a Python-based machine learning approach has delivered exceptional improvements in speed, scalability, and accuracy. This shift underscores a critical lesson: innovation in process mining comes from continuous evolution and leveraging modern data science techniques.