NeSy4PPM documentation — NeSy4PPM 0.1.0 documentation

# NeSy4PPM documentation

NeSy4PPM is the first Python package designed for both single-attribute (e.g., activity) and multi-attribute (e.g., activity and resource) suffix prediction in predictive process monitoring. It implements a Neuro-Symbolic (NeSy) system that integrates neural models with various types of symbolic background knowledge (BK), enabling accurate and compliant predictions even under concept drift.

NeSy4PPM offers the following key features:

1. Symbolic knowledge integration: supports declarative and procedural BK, including DECLARE, MP-DECLARE (multi-perspective DECLARE), ProbDECLARE (probabilistic DECLARE), and Petri nets.

2. Flexible learning: provides multiple prefix encoding methods and supports LSTM (Long Short-Term Memory) and Transformer architectures.

3. Drift-aware prediction: contextualizes neural predictions using BK in real-time, enhancing prediction accuracy and compliance in dynamic environments.

Contents:

- NeSy4PPM
  - NeSy4PPM package
- Installation
- Tutorials
  - NeSy4PPM: Suffix prediction Tutorial
  - 1 Data preprocessing
  - 2 Training
  - 3 Prediction
  - 4 Evaluation
- Repository Structure