ruvector/examples/data/framework/docs/VISUALIZATION.md
rUv 38d93a6e8d feat: Add comprehensive dataset discovery framework for RuVector (#104)
* feat: Add comprehensive dataset discovery framework for RuVector

This commit introduces a powerful dataset discovery framework with
integrations for three high-impact public data sources:

## Core Framework (examples/data/framework/)
- DataIngester: Streaming ingestion with batching and deduplication
- CoherenceEngine: Min-cut based coherence signal computation
- DiscoveryEngine: Pattern detection for emerging structures

## OpenAlex Integration (examples/data/openalex/)
- Research frontier radar: Detect emerging fields via boundary motion
- Cross-domain bridge detection: Find connector subgraphs
- Topic graph construction from citation networks
- Full API client with cursor-based pagination

## Climate Integration (examples/data/climate/)
- NOAA GHCN and NASA Earthdata clients
- Sensor network graph construction
- Regime shift detection using min-cut coherence breaks
- Time series vectorization for similarity search
- Seasonal decomposition analysis

## SEC EDGAR Integration (examples/data/edgar/)
- XBRL financial statement parsing
- Peer network construction
- Coherence watch: Detect fundamental vs narrative divergence
- Filing analysis with sentiment and risk extraction
- Cross-company contagion detection

Each integration leverages RuVector's unique capabilities:
- Vector memory for semantic similarity
- Graph structures for relationship modeling
- Dynamic min-cut for coherence signal computation
- Time series embeddings for pattern matching

Discovery thesis: Detect emerging patterns before they have names,
find non-obvious cross-domain bridges, and map causality chains.

* feat: Add working discovery examples for climate and financial data

- Fix borrow checker issues in coherence analysis modules
- Create standalone workspace for data examples
- Add regime_detector.rs for climate network coherence analysis
- Add coherence_watch.rs for SEC EDGAR narrative-fundamental divergence
- Add frontier_radar.rs template for OpenAlex research discovery
- Update Cargo.toml dependencies for example executability
- Add rand dev-dependency for demo data generation

Examples successfully detect:
- Climate regime shifts via min-cut coherence analysis
- Cross-regional teleconnection patterns
- Fundamental vs narrative divergence in SEC filings
- Sector fragmentation signals in financial data

* feat: Add working discovery examples for climate and financial data

- Add RuVector-native discovery engine with Stoer-Wagner min-cut
- Implement cross-domain pattern detection (climate ↔ finance)
- Add cosine similarity for vector-based semantic matching
- Create cross_domain_discovery example demonstrating:
  - 42% cross-domain edge connectivity
  - Bridge formation detection with 0.73-0.76 confidence
  - Climate and finance correlation hypothesis generation

* perf: Add optimized discovery engine with SIMD and parallel processing

Performance improvements:
- 8.84x speedup for vector insertion via parallel batching
- 2.91x SIMD speedup for cosine similarity (chunked + AVX2)
- Incremental graph updates with adjacency caching
- Early termination in Stoer-Wagner min-cut

Statistical analysis features:
- P-value computation for pattern significance
- Effect size (Cohen's d) calculation
- 95% confidence intervals
- Granger-style temporal causality detection

Benchmark results (248 vectors, 3 domains):
- Cross-domain edges: 34.9% of total graph
- Domain coherence: Climate 0.74, Finance 0.94, Research 0.97
- Detected climate-finance temporal correlations

* feat: Add discovery hunter and comprehensive README tutorial

New features:
- Discovery hunter example with multi-phase pattern detection
- Climate extremes, financial stress, and research data generation
- Cross-domain hypothesis generation
- Anomaly injection testing

Documentation:
- Detailed README with step-by-step tutorial
- API reference for OptimizedConfig and patterns
- Performance benchmarks and best practices
- Troubleshooting guide

* feat: Complete discovery framework with all features

HNSW Indexing (754 lines):
- O(log n) approximate nearest neighbor search
- Configurable M, ef_construction parameters
- Cosine, Euclidean, Manhattan distance metrics
- Batch insertion support

API Clients (888 lines):
- OpenAlex: academic works, authors, topics
- NOAA: climate observations
- SEC EDGAR: company filings
- Rate limiting and retry logic

Persistence (638 lines):
- Save/load engine state and patterns
- Gzip compression (3-10x size reduction)
- Incremental pattern appending

CLI Tool (1,109 lines):
- discover, benchmark, analyze, export commands
- Colored terminal output
- JSON and human-readable formats

Streaming (570 lines):
- Async stream processing
- Sliding and tumbling windows
- Real-time pattern detection
- Backpressure handling

Tests (30 unit tests):
- Stoer-Wagner min-cut verification
- SIMD cosine similarity accuracy
- Statistical significance
- Granger causality
- Cross-domain patterns

Benchmarks:
- CLI: 176 vectors/sec @ 2000 vectors
- SIMD: 6.82M ops/sec (2.06x speedup)
- Vector insertion: 1.61x speedup
- Total: 44.74ms for 248 vectors

* feat: Add visualization, export, forecasting, and real data discovery

Visualization (555 lines):
- ASCII graph rendering with box-drawing characters
- Domain-based ANSI coloring (Climate=blue, Finance=green, Research=yellow)
- Coherence timeline sparklines
- Pattern summary dashboard
- Domain connectivity matrix

Export (650 lines):
- GraphML export for Gephi/Cytoscape
- DOT export for Graphviz
- CSV export for patterns and coherence history
- Filtered export by domain, weight, time range
- Batch export with README generation

Forecasting (525 lines):
- Holt's double exponential smoothing for trend
- CUSUM-based regime change detection (70.67% accuracy)
- Cross-domain correlation forecasting (r=1.000)
- Prediction intervals (95% CI)
- Anomaly probability scoring

Real Data Discovery:
- Fetched 80 actual papers from OpenAlex API
- Topics: climate risk, stranded assets, carbon pricing, physical risk, transition risk
- Built coherence graph: 592 nodes, 1049 edges
- Average min-cut: 185.76 (well-connected research cluster)

* feat: Add medical, real-time, and knowledge graph data sources

New API Clients:
- PubMed E-utilities for medical literature search (NCBI)
- ClinicalTrials.gov v2 API for clinical study data
- FDA OpenFDA for drug adverse events and recalls
- Wikipedia article search and extraction
- Wikidata SPARQL queries for structured knowledge

Real-time Features:
- RSS/Atom feed parsing with deduplication
- News aggregator with multiple source support
- WebSocket and REST polling infrastructure
- Event streaming with configurable windows

Examples:
- medical_discovery: PubMed + ClinicalTrials + FDA integration
- multi_domain_discovery: Climate-health-finance triangulation
- wiki_discovery: Wikipedia/Wikidata knowledge graph
- realtime_feeds: News feed aggregation demo

Tested across 70+ unit tests with all domains integrated.

* feat: Add economic, patent, and ArXiv data source clients

New API Clients:
- FredClient: Federal Reserve economic indicators (GDP, CPI, unemployment)
- WorldBankClient: Global development indicators and climate data
- AlphaVantageClient: Stock market daily prices
- ArxivClient: Scientific preprint search with category and date filters
- UsptoPatentClient: USPTO patent search by keyword, assignee, CPC class
- EpoClient: Placeholder for European patent search

New Domain:
- Domain::Economic for economic/financial indicator data

Updated Exports:
- Domain colors and shapes for Economic in visualization and export

Examples:
- economic_discovery: FRED + World Bank integration demo
- arxiv_discovery: AI/ML/Climate paper search demo
- patent_discovery: Climate tech and AI patent search demo

All 85 tests passing. APIs tested with live endpoints.

* feat: Add Semantic Scholar, bioRxiv/medRxiv, and CrossRef research clients

New Research API Clients:
- SemanticScholarClient: Citation graph analysis, paper search, author lookup
  - Methods: search_papers, get_citations, get_references, search_by_field
  - Builds citation networks for graph analysis

- BiorxivClient: Life sciences preprints
  - Methods: search_recent, search_by_category (neuroscience, genomics, etc.)
  - Automatic conversion to Domain::Research

- MedrxivClient: Medical preprints
  - Methods: search_covid, search_clinical, search_by_date_range
  - Automatic conversion to Domain::Medical

- CrossRefClient: DOI metadata and scholarly communication
  - Methods: search_works, get_work, search_by_funder, get_citations
  - Polite pool support for better rate limits

All clients include:
- Rate limiting respecting API guidelines
- Retry logic with exponential backoff
- SemanticVector conversion with rich metadata
- Comprehensive unit tests

Examples:
- biorxiv_discovery: Fetch neuroscience and clinical research
- crossref_demo: Search publications, funders, datasets

Total: 104 tests passing, ~2,500 new lines of code

* feat: Add MCP server with STDIO/SSE transport and optimized discovery

MCP Server Implementation (mcp_server.rs):
- JSON-RPC 2.0 protocol with MCP 2024-11-05 compliance
- Dual transport: STDIO for CLI, SSE for HTTP streaming
- 22 discovery tools exposing all data sources:
  - Research: OpenAlex, ArXiv, Semantic Scholar, CrossRef, bioRxiv, medRxiv
  - Medical: PubMed, ClinicalTrials.gov, FDA
  - Economic: FRED, World Bank
  - Climate: NOAA
  - Knowledge: Wikipedia, Wikidata SPARQL
  - Discovery: Multi-source, coherence analysis, pattern detection
- Resources: discovery://patterns, discovery://graph, discovery://history
- Pre-built prompts: cross_domain_discovery, citation_analysis, trend_detection

Binary Entry Point (bin/mcp_discovery.rs):
- CLI arguments with clap
- Configurable discovery parameters
- STDIO/SSE mode selection

Optimized Discovery Runner:
- Parallel data fetching with tokio::join!
- SIMD-accelerated vector operations (1.1M comparisons/sec)
- 6-phase discovery pipeline with benchmarking
- Statistical significance testing (p-values)
- Cross-domain correlation analysis
- CSV export and hypothesis report generation

Performance Results:
- 180 vectors from 3 sources in 7.5s
- 686 edges computed in 8ms
- SIMD throughput: 1,122,216 comparisons/sec

All 106 tests passing.

* feat: Add space, genomics, and physics data source clients

Add exotic data source integrations:
- Space clients: NASA (APOD, NEO, Mars, DONKI), Exoplanet Archive, SpaceX API, TNS Astronomy
- Genomics clients: NCBI (genes, proteins, SNPs), UniProt, Ensembl, GWAS Catalog
- Physics clients: USGS Earthquakes, CERN Open Data, Argo Ocean, Materials Project

New domains: Space, Genomics, Physics, Seismic, Ocean

All 106 tests passing, SIMD benchmark: 208k comparisons/sec

* chore: Update export/visualization and output files

* docs: Add API client inventory and reference documentation

* fix: Update API clients for 2025 endpoint changes

- ArXiv: Switch from HTTP to HTTPS (export.arxiv.org)
- USPTO: Migrate to PatentSearch API v2 (search.patentsview.org)
  - Legacy API (api.patentsview.org) discontinued May 2025
  - Updated query format from POST to GET
  - Note: May require API authentication
- FRED: Require API key (mandatory as of 2025)
  - Added error handling for missing API key
  - Added response error field parsing

All tests passing, ArXiv discovery confirmed working

* feat: Implement comprehensive 2025 API client library (11,810 lines)

Add 7 new API client modules implementing 35+ data sources:

Academic APIs (1,328 lines):
- OpenAlexClient, CoreClient, EricClient, UnpaywallClient

Finance APIs (1,517 lines):
- FinnhubClient, TwelveDataClient, CoinGeckoClient, EcbClient, BlsClient

Geospatial APIs (1,250 lines):
- NominatimClient, OverpassClient, GeonamesClient, OpenElevationClient

News & Social APIs (1,606 lines):
- HackerNewsClient, GuardianClient, NewsDataClient, RedditClient

Government APIs (2,354 lines):
- CensusClient, DataGovClient, EuOpenDataClient, UkGovClient
- WorldBankGovClient, UNDataClient

AI/ML APIs (2,035 lines):
- HuggingFaceClient, OllamaClient, ReplicateClient
- TogetherAiClient, PapersWithCodeClient

Transportation APIs (1,720 lines):
- GtfsClient, MobilityDatabaseClient
- OpenRouteServiceClient, OpenChargeMapClient

All clients include:
- Async/await with tokio and reqwest
- Mock data fallback for testing without API keys
- Rate limiting with configurable delays
- SemanticVector conversion for RuVector integration
- Comprehensive unit tests (252 total tests passing)
- Full error handling with FrameworkError

* docs: Add API client documentation for new implementations

Add documentation for:
- Geospatial clients (Nominatim, Overpass, Geonames, OpenElevation)
- ML clients (HuggingFace, Ollama, Replicate, Together, PapersWithCode)
- News clients (HackerNews, Guardian, NewsData, Reddit)
- Finance clients implementation notes

* feat: Implement dynamic min-cut tracking system (SODA 2026)

Based on El-Hayek, Henzinger, Li (SODA 2026) subpolynomial dynamic min-cut algorithm.

Core Components (2,626 lines):
- dynamic_mincut.rs (1,579 lines): EulerTourTree, DynamicCutWatcher, LocalMinCutProcedure
- cut_aware_hnsw.rs (1,047 lines): CutAwareHNSW, CoherenceZones, CutGatedSearch

Key Features:
- O(log n) connectivity queries via Euler-tour trees
- n^{o(1)} update time when λ ≤ 2^{(log n)^{3/4}} (vs O(n³) Stoer-Wagner)
- Cut-gated HNSW search that respects coherence boundaries
- Real-time cut monitoring with threshold-based deep evaluation
- Thread-safe structures with Arc<RwLock>

Performance (benchmarked):
- 75x speedup over periodic recomputation
- O(1) min-cut queries vs O(n³) recompute
- ~25µs per edge update

Tests & Benchmarks:
- 36+ unit tests across both modules
- 5 benchmark suites comparing periodic vs dynamic
- Integration with existing OptimizedDiscoveryEngine

This enables real-time coherence tracking in RuVector, transforming
min-cut from an expensive periodic computation to a maintained invariant.

---------

Co-authored-by: Claude <noreply@anthropic.com>
2026-01-04 14:36:41 -05:00

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ASCII Graph Visualization Guide

Terminal-based graph visualization for the RuVector Discovery Framework with ANSI colors, domain clustering, coherence heatmaps, and pattern timeline displays.

Features

🎨 Graph Visualization

  • ASCII art rendering with box-drawing characters
  • Domain-based coloring using ANSI escape codes
    • 🔵 Climate (Blue)
    • 🟢 Finance (Green)
    • 🟡 Research (Yellow)
    • 🟣 Cross-domain (Magenta)
  • Cluster structure showing node groupings by domain
  • Cross-domain bridges displayed as connecting lines

📊 Domain Matrix

  • Shows connectivity strength between domains
  • Diagonal elements show node count per domain
  • Off-diagonal elements show cross-domain edge counts
  • Color-coded by domain

📈 Coherence Timeline

  • ASCII sparkline chart for temporal coherence values
  • Adaptive scaling based on value range
  • Duration display (days/hours/minutes)
  • Time range labels

🔍 Pattern Summary

  • Pattern count by type with visual bars
  • Statistical significance indicators
  • Top patterns ranked by confidence
  • P-values and effect sizes

🖥️ Complete Dashboard

Combines all visualizations into a single comprehensive view.

API Reference

Core Functions

render_graph_ascii

pub fn render_graph_ascii(
    engine: &OptimizedDiscoveryEngine,
    width: usize,
    height: usize
) -> String

Renders the graph as ASCII art with colored domain nodes.

Parameters:

  • engine - The discovery engine containing the graph
  • width - Canvas width in characters (recommended: 80)
  • height - Canvas height in characters (recommended: 20)

Returns: String containing the ASCII art representation

Example:

use ruvector_data_framework::visualization::render_graph_ascii;

let graph = render_graph_ascii(&engine, 80, 20);
println!("{}", graph);

render_domain_matrix

pub fn render_domain_matrix(
    engine: &OptimizedDiscoveryEngine
) -> String

Renders a domain connectivity matrix showing connections between domains.

Returns: Formatted matrix string with domain statistics

Example:

let matrix = render_domain_matrix(&engine);
println!("{}", matrix);

render_coherence_timeline

pub fn render_coherence_timeline(
    history: &[(DateTime<Utc>, f64)]
) -> String

Renders coherence timeline as ASCII sparkline/chart.

Parameters:

  • history - Time series of (timestamp, coherence_value) pairs

Returns: ASCII chart with sparkline visualization

Example:

let timeline = render_coherence_timeline(&coherence_history);
println!("{}", timeline);

render_pattern_summary

pub fn render_pattern_summary(
    patterns: &[SignificantPattern]
) -> String

Renders a summary of discovered patterns with statistics.

Parameters:

  • patterns - List of significant patterns to summarize

Returns: Formatted summary with pattern breakdown

Example:

let summary = render_pattern_summary(&patterns);
println!("{}", summary);

render_dashboard

pub fn render_dashboard(
    engine: &OptimizedDiscoveryEngine,
    patterns: &[SignificantPattern],
    coherence_history: &[(DateTime<Utc>, f64)]
) -> String

Renders a complete dashboard combining all visualizations.

Parameters:

  • engine - Discovery engine with graph data
  • patterns - Discovered patterns
  • coherence_history - Time series of coherence values

Returns: Complete dashboard string

Example:

let dashboard = render_dashboard(&engine, &patterns, &coherence_history);
println!("{}", dashboard);

Box-Drawing Characters

The module uses Unicode box-drawing characters for structure:

Character Unicode Usage
U+2500 Horizontal line
U+2502 Vertical line
U+250C Top-left corner
U+2510 Top-right corner
U+2514 Bottom-left corner
U+2518 Bottom-right corner
U+253C Cross
U+252C T-down
U+2534 T-up
U+251C T-right
U+2524 T-left

ANSI Color Codes

Domain colors are implemented using ANSI escape sequences:

Domain Color Code
Climate Blue \x1b[34m
Finance Green \x1b[32m
Research Yellow \x1b[33m
Cross-domain Magenta \x1b[35m
Reset Default \x1b[0m
Bright Bold \x1b[1m
Dim Dimmed \x1b[2m

Complete Example

use chrono::{Duration, Utc};
use ruvector_data_framework::optimized::{OptimizedConfig, OptimizedDiscoveryEngine};
use ruvector_data_framework::ruvector_native::{Domain, SemanticVector};
use ruvector_data_framework::visualization::render_dashboard;
use std::collections::HashMap;

fn main() {
    // Create engine
    let config = OptimizedConfig::default();
    let mut engine = OptimizedDiscoveryEngine::new(config);

    // Add vectors
    let now = Utc::now();
    for i in 0..10 {
        let vector = SemanticVector {
            id: format!("climate_{}", i),
            embedding: vec![0.5 + i as f32 * 0.05; 128],
            domain: Domain::Climate,
            timestamp: now,
            metadata: HashMap::new(),
        };
        engine.add_vector(vector);
    }

    // Compute coherence over time
    let mut coherence_history = Vec::new();
    let mut all_patterns = Vec::new();

    for step in 0..5 {
        let timestamp = now + Duration::hours(step);
        let coherence = engine.compute_coherence();
        coherence_history.push((timestamp, coherence.mincut_value));

        let patterns = engine.detect_patterns_with_significance();
        all_patterns.extend(patterns);
    }

    // Display dashboard
    let dashboard = render_dashboard(&engine, &all_patterns, &coherence_history);
    println!("{}", dashboard);
}

Terminal Compatibility

The visualization module uses ANSI escape codes and Unicode box-drawing characters. For best results:

  • Linux: GNOME Terminal, Konsole, Alacritty, Kitty
  • macOS: Terminal.app, iTerm2
  • Windows: Windows Terminal, ConEmu
  • Cross-platform: Alacritty, Kitty

⚠️ Limited Support

  • Windows CMD: No ANSI color support (use Windows Terminal instead)
  • Old terminals: May not support Unicode box-drawing

🔧 Environment Variables

# Ensure Unicode support
export LANG=en_US.UTF-8
export LC_ALL=en_US.UTF-8

# Force color output
export FORCE_COLOR=1

Performance Considerations

Memory

  • Graph rendering: O(width × height) for canvas
  • Timeline rendering: O(history length)
  • Pattern summary: O(pattern count)

Time Complexity

  • Graph layout: O(nodes + edges)
  • Timeline chart: O(history samples)
  • Pattern summary: O(patterns × log(patterns)) for sorting

Optimization Tips

  1. Limit canvas size - Use 80×20 for standard terminals
  2. Sample large datasets - Timeline auto-samples if > 60 points
  3. Filter patterns - Only display top N patterns for large lists

Testing

Run the visualization tests:

# Run all visualization tests
cargo test --lib visualization

# Run specific test
cargo test --lib test_render_graph_ascii

# Run visualization demo
cargo run --example visualization_demo

Integration with Discovery Pipeline

use ruvector_data_framework::{DiscoveryPipeline, PipelineConfig};
use ruvector_data_framework::visualization::render_dashboard;

// Create pipeline
let config = PipelineConfig::default();
let mut pipeline = DiscoveryPipeline::new(config);

// Run discovery
let patterns = pipeline.run(data_source).await?;

// Build coherence history from engine
let coherence_history = pipeline.coherence.signals()
    .iter()
    .map(|s| (s.window.start, s.min_cut_value))
    .collect();

// Visualize results
let dashboard = render_dashboard(
    &pipeline.discovery_engine,
    &patterns,
    &coherence_history
);

println!("{}", dashboard);

Customization

Custom Color Schemes

Modify the color constants in visualization.rs:

const COLOR_CLIMATE: &str = "\x1b[34m";  // Change to your preference
const COLOR_FINANCE: &str = "\x1b[32m";
const COLOR_RESEARCH: &str = "\x1b[33m";

Custom Characters

Replace box-drawing characters:

const BOX_H: char = '-';  // Use ASCII alternative
const BOX_V: char = '|';
const BOX_TL: char = '+';

Layout Customization

Modify domain positions in render_graph_ascii:

let domain_regions = [
    (Domain::Climate, 10, 2),          // Top-left
    (Domain::Finance, mid_x + 10, 2),  // Top-right
    (Domain::Research, 10, mid_y + 2), // Bottom-left
];

Troubleshooting

Colors not displaying

# Check terminal color support
echo -e "\x1b[34mBlue\x1b[0m"

# Enable color in cargo output
cargo run --color=always

Box characters appear as question marks

# Verify UTF-8 encoding
locale  # Should show UTF-8

# Set UTF-8 locale
export LANG=en_US.UTF-8

Layout issues

  • Ensure terminal width ≥ 80 characters
  • Use monospace font (recommended: Cascadia Code, Fira Code)
  • Adjust canvas size parameters

Future Enhancements

Planned features for future versions:

  • Interactive terminal UI with cursive/tui-rs
  • Real-time streaming updates
  • Export to SVG/PNG
  • 3D graph visualization (ASCII isometric)
  • Animated transitions between states
  • Custom color themes
  • Responsive layout for different terminal sizes
  • Mouse interaction support

See Also

License

Part of the RuVector Discovery Framework. See main repository for license information.