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glam/docs/plan/frontend/00-overview.md
kempersc fa5680f0dd Add initial versions of custodian hub UML diagrams in Mermaid and PlantUML formats 
- Introduced custodian_hub_v3.mmd, custodian_hub_v4_final.mmd, and custodian_hub_v5_FINAL.mmd for Mermaid representation.
- Created custodian_hub_FINAL.puml and custodian_hub_v3.puml for PlantUML representation.
- Defined entities such as CustodianReconstruction, Identifier, TimeSpan, Agent, CustodianName, CustodianObservation, ReconstructionActivity, Appellation, ConfidenceMeasure, Custodian, LanguageCode, and SourceDocument.
- Established relationships and associations between entities, including temporal extents, observations, and reconstruction activities.
- Incorporated enumerations for various types, statuses, and classifications relevant to custodians and their activities.
2025-11-22 14:33:51 +01:00

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Heritage Custodian Frontend Implementation Plan

Executive Summary

This document outlines the implementation plan for a web-based frontend application to visualize and explore Heritage Custodian Linked Data from the RDF schemas located in /schemas/20251121/rdf/. The frontend will be built using modern web technologies, leveraging patterns from the example_ld project while extending them for the specific needs of heritage institution data visualization.

Project Objectives

Primary Goals

  1. Interactive RDF Visualization: Display Heritage Custodian data from multiple RDF formats
  2. UML Diagram Rendering: Convert Mermaid/PlantUML diagrams to interactive D3.js visualizations
  3. SPARQL Query Interface: Enable semantic queries against the RDF data
  4. Multi-format Support: Handle Turtle, JSON-LD, N-Triples, RDF/XML, and other RDF serializations
  5. Responsive Design: Mobile-first approach for accessibility across devices

Secondary Goals

  1. Data Export: Enable users to download filtered/queried data in various formats
  2. Provenance Tracking: Visualize data lineage and quality tiers
  3. Geographic Mapping: Interactive maps showing institution locations
  4. Temporal Navigation: Timeline views for organizational change events
  5. Network Visualization: Display relationships between institutions

Key Deliverables

1. Frontend Application

  • React/TypeScript single-page application
  • Component library for RDF visualization
  • D3.js integration for complex visualizations
  • SPARQL query builder interface

2. Backend Services

  • RDF triple store (Apache Jena Fuseki or Oxigraph)
  • GraphQL API for efficient data fetching
  • WebSocket support for real-time updates
  • Caching layer for performance

3. Documentation

  • User guide for navigation and features
  • Developer documentation for extension
  • API documentation for backend services
  • Deployment guide for production setup

Technology Stack

Frontend

  • Framework: React 18 with TypeScript
  • State Management: Zustand or Redux Toolkit
  • Styling: Tailwind CSS + Shadcn/ui components
  • Visualization: D3.js v7, Cytoscape.js, Leaflet
  • RDF Processing: N3.js, rdflib.js
  • Build Tool: Vite

Backend

  • Runtime: Node.js 20 LTS or Deno
  • Framework: Fastify or Hono
  • RDF Store: Apache Jena Fuseki or Oxigraph
  • Cache: Redis
  • Database: PostgreSQL with PostGIS for geographic data

Infrastructure

  • Container: Docker with multi-stage builds
  • Orchestration: Docker Compose for development
  • CI/CD: GitHub Actions
  • Monitoring: Prometheus + Grafana

Implementation Phases

Phase 1: Foundation (Weeks 1-2)

  • Project setup and configuration
  • Basic RDF loading and parsing
  • Initial component library
  • Simple list and detail views

Phase 2: Core Visualization (Weeks 3-4)

  • D3.js integration for UML diagrams
  • Network graph visualization
  • Geographic mapping
  • Timeline components

Phase 3: Query Interface (Weeks 5-6)

  • SPARQL query builder
  • Faceted search
  • Advanced filtering
  • Results visualization

Phase 4: Enhancement (Weeks 7-8)

  • Performance optimization
  • Export functionality
  • User preferences
  • Documentation

Phase 5: Production (Week 9)

  • Deployment setup
  • Monitoring integration
  • Security hardening
  • Launch preparation

Success Metrics

1. Performance

  • Initial load time < 3 seconds
  • RDF parsing < 500ms for 10,000 triples
  • Smooth 60fps interactions

2. Usability

  • Mobile responsive design
  • WCAG 2.1 AA compliance
  • Intuitive navigation

3. Functionality

  • Support for all RDF formats in /schemas/20251121/rdf/
  • Complete SPARQL 1.1 query support
  • Export in 5+ formats

Risk Mitigation

Risk Impact Mitigation
Large RDF files causing performance issues High Implement pagination, virtualization, and progressive loading
Complex SPARQL queries timing out Medium Query optimization, caching, timeout limits
Cross-browser compatibility Medium Progressive enhancement, polyfills, thorough testing
D3.js learning curve Low Leverage existing examples, consider alternative libraries

Team Structure

  • Frontend Developer: React components, D3.js visualizations
  • Backend Developer: RDF store, API development
  • UX Designer: Interface design, user flows
  • DevOps Engineer: Infrastructure, CI/CD
  • QA Engineer: Testing, accessibility

Timeline

  • Week 1-2: Foundation phase
  • Week 3-4: Core visualization
  • Week 5-6: Query interface
  • Week 7-8: Enhancement
  • Week 9: Production deployment
  • Week 10+: Maintenance and iteration

Next Steps

  1. Review and approve implementation plan
  2. Set up development environment
  3. Create initial project structure
  4. Begin Phase 1 implementation

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