Introduction

This page provides technical details about the GEM architecture, data formats, and system components for developers and technical users.

System architecture

GEM consists of several integrated components:

GEM dashboard application (UI)

• Framework: Web-based interactive interface
• Deployment: Hosted on Dashboard (my.tomtom.com/gem)
• Authentication: Microsoft Entra ID (Azure AD)
• Authorization: Role-based access control

Backend services

• Map Matching Engine: Advanced algorithms for road network matching
• Pipeline Orchestration: Automated job processing and management
• Job Management: Status tracking and results generation
• Storage: Secure cloud storage for data management

Authorization and security

Access control

GEM uses role-based access control for secure operations:

Permission Levels:

Resource Authorization:

• Job runs: Users can only access their own jobs
• Storage: Access controlled by your organization's permissions
• Credentials: Temporary, scoped to specific operations

Authentication flow

1. User Authentication: Microsoft Entra ID via Dashboard
2. Storage Authorization: System validates storage access
3. Credential Generation: Temporary tokens for Azure CLI

Performance metrics

System performance

Based on production metrics:

• Uptime: ≥99% (continuously monitored)
• Processing Speed: ~100,000 road segments matched per hour
• Matching Accuracy: >85% confidence scores for high-quality input data
• System Availability: Deployed on production cluster with Helm

Performance monitoring

Performance is continuously monitored to ensure:

• High system uptime
• Fast job execution
• Accurate matching results
• Minimal errors

Metrics tracked:

• System uptime
• Job execution time
• Matching accuracy
• Error rates

Access requirements

Access to GEM is controlled by your organization and project assignments. Access the service at my.tomtom.com/gem. Contact your system administrator if you need access to GEM.

Matching algorithms

AI-driven matching

GEM uses advanced algorithms for map matching:

• Algorithm Type: AI-driven road network matching
• Matching Strategy: Geometry-based with topological validation
• Confidence Scoring: Probabilistic confidence for each match
• Sub-Segment Precision: Linear referencing for detailed attribution

Matching types

Currently supported:

• ROAD_MATCHING: Match road network data to Overture Maps road segments
• LANE_LEVEL_MATCHING: Match lane-level data to Overture Maps lane segments (in progress)

Reference map data

Overture Maps integration

GEM matches against Overture Maps Foundation datasets:

• GERS IDs: Global Entity Reference System identifiers
• Road Network: Comprehensive global road coverage
• Update Frequency: Periodic releases from Overture Foundation
• Data Quality: Community-validated and continuously improved

Technical requirements

Client requirements

• Azure CLI: Latest version installed locally
• Network: Stable internet connection for large file transfers
• Storage: Sufficient local disk space for data files
• Browser: Modern web browser for UI access (Chrome, Firefox, Safari, Edge)

Data requirements

• Input Size: No theoretical limit (Azure CLI handles any file size)
• Format Compliance: Must be valid Parquet with required schema
• Geometry Format: Valid WKT LineString geometries
• Data Quality: Better input quality leads to higher matching confidence

Security and compliance

Data security

• Encryption in Transit: TLS 1.2+ for all communications
• Encryption at Rest: Azure Blob Storage encryption
• Credential Security: Temporary tokens with limited scope
• Access Logging: Comprehensive audit trails

Compliance

• Quality Assurance: Enterprise-grade code quality scanning
• Security Scanning: Regular vulnerability detection and patching
• Database Security: Encrypted storage with network isolation

Error handling

Common error scenarios

Additional resources

• Getting Started Guide
• UI Workflow Guide
• API Documentation
• Understanding GERS IDs
• Technical Details
• Azure CLI Documentation
• Azure Blob Storage Guide
• FAQ