Welcome to the Collaboration Pattern Detection Framework documentation!

Note

This project and documentation is under active development.

This Python3-based framework conducts Collaboration Pattern Detection using methods from Process Mining, Frequent Subgraph Mining & Graph Set Clustering techniques. Resulting (process) graph sets / patterns are stored in a Graph Collection and can be exported as a file, or uploaded/downloaded from or to a Neo4j graph database.

This framework is used in the Social Process Mining project to detect Collaboration Pattern inside Enterprise Collaboration Systems (ECS).

Overview

1. Preprocessing: This package provides an API for preprocessing the event logs and prepare them for the collaboration pattern detection discovery algorithm.

2. Process Mining: This package provides an API for mining graphs (process discovery) from event logs using Process Mining discovery algorithms.

3. Frequent Subgraph Mining: The generated graphs are used as input for Frequent Subgraph Mining.

4. Clustering Graph Sets: The resulting sub-graphs can be clustered using graph set clustering.

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Contents:

• Install
  • External requirements
  • Install python package
• Graph Collection
  • Create a graph
  • Graph Cluster
  • Import and export
  • Sync with a neo4j database
• Preprocessing
  • Event Log Converter
• Process Mining
  • Mining Process Graphs
  • Preprocessing
• Frequent Subgraph Mining
• Clustering Graph Sets
• API
  • GraphCollection
  • GraphMining
  • Frequent Subgraph Mining
  • Graph Set Clustering