README_PROCESS_MINING

Process Mining in AQL - Project Documentation

Version: 1.0
Date: 2025-12-24
Status: Research & Design Phase Complete ✅

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Overview

This documentation suite covers the complete design and implementation plan for integrating Process Mining capabilities into ThemisDB's Advanced Query Language (AQL).

Original Requirement

"Ich möchte das Process-mining auch in AQL möglich machen. eine Idee ist eine Ideal Prozessstruktur zu übergeben und die process-miner suchen nach graphen, vectoren, relationalen Zusammenhängen und zeigen entsprechende gefundene 'ähnliche' Prozesse an."

Translation: Enable process mining in AQL by passing an ideal process structure and having process miners search for similar processes using graph, vector, and relational patterns.

New Requirement

"Modelle für Verwaltung bevorzugt"

Translation: Prefer models for public administration/governance.

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📁 Documentation Structure

1. PROCESS_MINING_SUMMARY.md ⭐ START HERE

Executive Summary - High-level overview of the entire project

Contents:

• Project objectives and achievements
• Deliverables summary (documentation, code, config)
• Architecture overview
• Quick reference to all other documents
• Next steps

Audience: Project managers, stakeholders, new team members

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2. PROCESS_MINING_RESEARCH_AND_ROADMAP.md

Comprehensive Research - 50+ pages of scientific foundation and implementation plan

Contents:

• Section 1: Scientific foundations (11 publications)
• Section 2: Existing ThemisDB features
• Section 3: Requirements analysis
• Section 4: Solution architecture
• Section 5: Implementation roadmap (6 phases)
• Section 6: Example use cases
• Section 7: Scientific publications reference
• Section 8: Success criteria
• Section 9: Risk analysis

Key Topics:

• Alpha Miner, Heuristic Miner, Inductive Miner algorithms
• Graph Edit Distance, Jaccard Similarity, LCS, Cosine Similarity
• Administrative process best practices
• Compliance frameworks (GWB, BauO, DSGVO, AGG)

Audience: Developers, researchers, architects

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3. PROCESS_MINING_AQL_EXAMPLES.md

Practical Examples - 10 complete real-world scenarios with AQL queries

Contents:

• Example 1: Finding similar building permit processes
• Example 2: Conformance checking against ideal process
• Example 3: Pattern-based filtering
• Example 4: Procurement process discovery
• Example 5: Process variant analysis (HR)
• Example 6: Bottleneck detection (Budget planning)
• Example 7: Predictive analytics (End time prediction)
• Example 8: List available administrative models
• Example 9: Export discovered process as BPMN
• Example 10: Complex analysis - Cross-department comparison

Also Includes:

• Tips & best practices
• Troubleshooting guide
• Performance optimization tips

Audience: Database users, analysts, administrators

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4. process_mining_guide.md

Feature Guide - Overview of process mining capabilities (existing document)

Contents:

• Process discovery algorithms
• Analysis capabilities (DFG, variants, bottlenecks)
• Conformance checking
• Export formats (BPMN, Petri Net)
• Usage examples

Audience: End users, tutorial readers

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💻 Code Documentation

1. process_pattern_matcher.h

Core Pattern Matching Class

class ProcessPatternMatcher {
    // Find processes similar to a pattern
    std::pair<Status, std::vector<SimilarityResult>> findSimilar(
        const ProcessPattern& pattern,
        const PatternMatchConfig& config
    );
    
    // Compare process with ideal model
    std::pair<Status, ConformanceResult> compareWithIdeal(
        const std::string& case_id,
        const ProcessPattern& ideal_pattern
    );
    
    // Check if process matches pattern
    std::pair<Status, bool> hasPattern(
        const std::string& case_id,
        const ProcessPattern& pattern,
        double threshold
    );
    
    // Load administrative models
    std::pair<Status, std::map<std::string, ProcessPattern>> 
        loadAdministrativeModels();
};

Features:

• 4 similarity methods: GRAPH, VECTOR, BEHAVIORAL, HYBRID
• Pattern caching for performance
• Integration with VectorIndex and GraphIndex
• Batch operations support

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2. process_mining_functions.h

AQL Function Definitions

15 New AQL Functions:

Pattern Matching (NEW):

• PM_FIND_SIMILAR(pattern, config) → Array
• PM_COMPARE_IDEAL(case_id, ideal) → Object
• PM_HAS_PATTERN(case_id, pattern, threshold) → Boolean

Event Log:

• PM_EXTRACT_LOG(collection, config) → Object
• PM_EXTRACT_TRACE(case_id) → Object

Discovery:

• PM_DISCOVER_PROCESS(log, config) → Object
• PM_VARIANTS(log, top_n) → Array

Administrative Models (NEW):

• PM_LOAD_ADMIN_MODEL(model_id) → Object
• PM_LIST_ADMIN_MODELS() → Array

Conformance:

• PM_CONFORMANCE(case_id, model) → Object
• PM_DEVIATIONS(case_id, model) → Array

Performance:

• PM_BOTTLENECKS(log, threshold) → Array
• PM_PREDICT_END(case_id) → Object

Export:

• PM_EXPORT_BPMN(model) → String

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⚙️ Configuration

administrative_process_models.yaml

Predefined Administrative Process Models

5 Models Included:

1. bauantrag_standard - Building Permit Process

   • §34 BauO compliant
   • 3-month SLA
   • Vier-Augen-Prinzip

2. beschaffung_vergaberecht - Procurement Process

   • GWB and VOB/A compliant
   • EU threshold rules (€214,000)
   • Documentation requirements

3. personal_einstellung - HR Recruitment

   • AGG compliant (anti-discrimination)
   • DSGVO compliant (data protection)
   • Betriebsrat integration

4. haushaltsplanung_jaehrlich - Budget Planning

   • Annual cycle
   • Fixed milestones (30.09., 15.10., 30.11.)
   • Multi-level approval

5. dokumenten_freigabe - Document Approval

   • Multi-stage workflow
   • Version control
   • Vier-Augen-Prinzip

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🎯 Quick Start Guide

For Users: Run Your First Query

-- 1. List available administrative models
LET models = PM_LIST_ADMIN_MODELS()
RETURN models

-- 2. Load a specific model
LET ideal = PM_LOAD_ADMIN_MODEL("bauantrag_standard")
RETURN ideal

-- 3. Find similar processes
LET similar = PM_FIND_SIMILAR(ideal, {
  method: "hybrid",
  threshold: 0.75,
  limit: 10
})
RETURN similar

See: PROCESS_MINING_AQL_EXAMPLES.md for 10 complete examples.

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For Developers: Implementation Guide

1. Read Research Document

   • PROCESS_MINING_RESEARCH_AND_ROADMAP.md
   • Understand algorithms and metrics

2. Review API Design

   • process_pattern_matcher.h
   • process_mining_functions.h

3. Follow Implementation Roadmap

   • Phase 2: Core Implementation
   • Phase 3: AQL Integration
   • Phase 4: Administrative Models
   • Phase 5: Testing
   • Phase 6: Optimization

4. Run Tests

   • Unit tests (target: 85%+ coverage)
   • Integration tests
   • Performance benchmarks

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📊 Project Status

Phase 1: Research & Design ✅ COMPLETED

Duration: Week 1-2
Status: 100% Complete

Deliverables:

• 50+ pages research document
• 11 scientific publications referenced
• API design complete
• 15 AQL functions specified
• 5 administrative models defined
• 10 usage examples created
• Architecture diagrams

Phase 2: Core Implementation ⏳ NEXT

Duration: Week 3-4
Status: Not Started

Tasks:

• Implement ProcessPatternMatcher class
• Graph similarity algorithms
• Vector similarity with embeddings
• Behavioral similarity metrics
• Unit tests

Phase 3-6: See Roadmap

Status: Planned

See: PROCESS_MINING_RESEARCH_AND_ROADMAP.md Section 5 for full roadmap.

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🔬 Scientific Foundation

Key Publications

1. van der Aalst, W.M.P. (2016)
   Process Mining: Data Science in Action

2. Dijkman et al. (2011)
   Similarity of Business Process Models: Metrics and Evaluation

3. Weidlich et al. (2011)
   Behavioural Profiles for Business Process Models

4. Evermann et al. (2017)
   Predicting Process Behaviour Using Deep Learning

Complete List: See PROCESS_MINING_RESEARCH_AND_ROADMAP.md Section 7.

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🏛️ Administrative Focus

Compliance Frameworks Supported

Building & Construction:

• §34 BauO (Building regulations)
• Dokumentationspflicht

Procurement:

• GWB §119 (Public procurement)
• VOB/A (Construction contracts)

HR & Privacy:

• AGG (Anti-discrimination)
• DSGVO/GDPR (Data protection)

General Administration:

• Vier-Augen-Prinzip (Four-eyes principle)
• Haushaltsrecht (Budget law)

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🎓 Use Cases

Public Administration

• Building permit conformance checking
• Procurement process compliance (GWB)
• HR recruitment (AGG/DSGVO compliant)
• Budget planning optimization
• Document approval workflows

Industry

• Production process optimization
• Quality assurance workflows
• Supply chain process mining

Healthcare

• Patient pathway analysis
• Treatment process discovery
• Clinical compliance checking

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📈 Performance & Scalability

Design Targets

• 100k processes: Similarity search < 1 second
• 1M processes: Incremental indexing < 5 seconds
• Cache hit rate: > 80% for frequent patterns

Optimization Strategies

• HNSW indexing for vector search
• Pattern caching
• Parallel processing (OpenMP)
• GPU acceleration (optional, CUDA)

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🛠️ Technology Stack

Existing Components (Leverage)

• VectorIndex: HNSW, GNN embeddings
• GraphIndex: Graph analytics
• ProcessMining: Alpha/Heuristic/Inductive miners

New Components (To Be Implemented)

• ProcessPatternMatcher: Pattern matching engine
• AdministrativeModelLoader: YAML model loader
• SimilarityComputer: Graph/Vector/Behavioral metrics

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📚 Additional Resources

Internal Documentation

• AQL Grammar
• Function Registry
• Graph Index
• Vector Index

External Resources

• Process Mining Book (van der Aalst)
• ProM Tools
• BPMN Specification

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🤝 Contributing

For Questions

• See PROCESS_MINING_AQL_EXAMPLES.md Troubleshooting section
• Review PROCESS_MINING_RESEARCH_AND_ROADMAP.md for technical details

For Implementation

1. Review API design in header files
2. Follow coding standards from existing codebase
3. Write unit tests (target: 85%+ coverage)
4. Update documentation as needed

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📝 Changelog

Version 1.0 (2025-12-24)

• ✅ Complete research & design phase
• ✅ 4 documentation files created (100+ pages)
• ✅ 2 API header files designed
• ✅ 1 configuration file with 5 models
• ✅ 15 AQL functions specified
• ✅ 11 scientific publications referenced
• ✅ 10 usage examples provided

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📞 Contact & Support

Project: ThemisDB Process Mining AQL Integration
Phase: Research & Design Complete
Version: 1.0
Date: 2025-12-24

Team: ThemisDB Development Team
Repository: makr-code/ThemisDB

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🎯 Summary

This project delivers a comprehensive design for integrating process mining into AQL with:

• ✅ Scientific foundation (11 publications)
• ✅ Practical examples (10 scenarios)
• ✅ Administrative focus (5 governance models)
• ✅ Multi-model similarity (graph + vector + behavioral)
• ✅ Production-ready architecture
• ✅ Clear implementation roadmap

Status: Ready for implementation phase 🚀

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Last Updated: 2025-12-24
Next Review: Start of Implementation Phase 2