process_mining_guide

Process Mining Guide

Version: v1.3.0 Phase 2
Status: Production-Ready
Last Updated: December 22, 2025

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Overview

ThemisDB's Process Mining module enables automated discovery and analysis of business processes from event log data. It provides a comprehensive suite of algorithms for process discovery, conformance checking, and performance enhancement.

Key Features

Process Discovery Algorithms

• Alpha Miner: Classical algorithm for structured processes
• Heuristic Miner: Robust algorithm handling noise and exceptions
• Inductive Miner: Sound process models with guaranteed fitness

Analysis Capabilities

• Directly-Follows Graph (DFG): Visualize process flows with frequencies
• Variant Analysis: Identify common and rare process execution patterns
• Bottleneck Detection: Find performance issues and delays
• Social Network Mining: Analyze collaboration patterns between resources

Conformance Checking

• Token replay for fitness calculation
• Deviation detection and analysis
• Model-reality alignment

Export Formats

• BPMN 2.0 XML for standard process modeling
• Petri Net (PNML) for formal analysis
• JSON for programmatic access

Usage Examples

Basic Process Discovery

ProcessMining mining(db);

// Extract event log from collection
EventLogConfig config;
config.case_id_field = "order_id";
config.activity_field = "action";
config.timestamp_field = "timestamp";

auto event_log = mining.extractEventLog("audit_log", config);

// Discover process model
auto model = mining.discoverProcess(event_log, MiningAlgorithm::HEURISTIC);

// Export to BPMN
std::string bpmn = mining.exportToBPMN(model);

Variant Analysis

auto variants = mining.analyzeVariants(event_log);

// Find most common process variant
auto most_common = variants[0];
std::cout << "Most common variant occurs " << most_common.frequency << " times" << std::endl;

Bottleneck Detection

auto bottlenecks = mining.detectBottlenecks(event_log, 0.9); // 90th percentile

for (const auto& bottleneck : bottlenecks) {
    std::cout << "Bottleneck: " << bottleneck.activity 
              << " (avg: " << bottleneck.avg_duration_ms << "ms)" << std::endl;
}

Social Network Analysis

auto social_network = mining.extractSocialNetwork(event_log);
auto handovers = mining.analyzeHandovers(social_network);

// Analyze collaboration patterns
auto metrics = mining.calculateCollaborationMetrics(social_network);

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See full documentation at https://github.com/makr-code/ThemisDB

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Last Updated: December 22, 2025
Version: v1.3.0 Phase 2
Status: Production-Ready