vidyaastra

🚀 Vidya Astra - Knowledge Toolkit

“Vidya Astra: The Toolkit of Knowledge” - Powerful, scalable knowledge graphs for the modern era

🌐 Live Interactive Demos

Explore Vidya Astra’s knowledge graphs in action:

Demo	Description	Link
🏹 Mahabharata Epic	Mahabharat 64 Main Characters and 2700 , several events !	View Demo
🐒 Ramayana Epic	Ramayana Epic Knowledge Graph	View Demo
☁️ Meghdoot	Kalidasa’s Meghdoot Knowledge Graph	View Demo
📜 Valayapathi	Valayapathi Tamil Epic	View Demo
💞 Nala Damayanti	Nala Damayanti Epic Knowledge Graph	View Demo
🌸 Kundalakesi	Kundalakesi Tamil Epic Knowledge Graph	View Demo
🌿 Dushyant Shakuntala	Dushyant Shakuntala Epic Knowledge Graph	View Demo
🏰 Ponniyin Selvan	Ponniyin Selvan Epic Knowledge Graph	View Demo
🕊️ Samrat Ashoka	Samrat Ashoka Epic Knowledge Graph	View Demo
🔔 Cilappatikaram	Cilappatikaram Tamil Epic Knowledge Graph	View Demo
🐦 Bird Migration	Track global bird migration patterns	View Demo
🏥 Healthcare	Medical knowledge and drug interactions	View Demo
🔐 Cybersecurity	Security threat and vulnerability mapping	View Demo
🚂 Indian Railways	Railway network and route optimization	View Demo
🗽 New York Tourism	NYC attractions and travel recommendations	View Demo
🛒 Supermarket	Product catalog and customer behavior	View Demo
🧘 Yoga	Poses, benefits, and practice sequences	View Demo
🍽️ Recipe Graph	Culinary knowledge and food pairings	View Demo
🎵 Music Network	Artist collaborations and recommendations	View Demo
🏠 Who is Vishal Mysore	Interactive introduction to Yours Truly !	View Demo
⚔️ Game of Thrones	Houses, characters, and intricate relationships	View Demo
🌈 Wizard of Oz	Dorothy’s journey through the Land of Oz	View Demo
⚡ Harry Potter	Wizarding World with Houses, spells, and characters	View Demo
👑 Bahubali	Epic saga of Mahishmati kingdom	View Demo
🚨 Fraud Detection	Real-time fraud pattern visualization	View Demo
🔍 Cycle Detection	Detect fraud rings and circular patterns	View Demo

Vidya Astra Architecture

🎯 What is Vidya Astra?

Vidya Astra is a comprehensive, multi-domain knowledge graph platform that harnesses the power of Neo4j and Spring Boot to create intelligent, interconnected knowledge systems. It’s designed for organizations that need to:

🧠 Understand Complex Relationships across multiple domains
🔍 Detect Patterns and anomalies in connected data
🎓 Learn from Knowledge through interactive visualizations
⚡ Scale Intelligently with graph database technology
🌍 Adapt Across Domains using consistent architecture
💡 Make Better Decisions based on connected intelligence

📖 Etymology: Why “Vidya Astra”?

Vidya (विद्या)  =  Knowledge, Wisdom, Learning
Astra (अस्त्र)  =  Tool, Weapon, Instrument
───────────────────────────────────────────────
         ⚔️  "The Weapon of Knowledge"  ⚔️

🏗️ Architecture Overview

Vidya Astra combines multiple cutting-edge technologies:

┌─────────────────────────────────────────────┐
│         Interactive D3.js Visualizations    │
│    (Real-time Knowledge Graph Exploration)  │
└──────────────────┬──────────────────────────┘
                   │
┌──────────────────▼──────────────────────────┐
│       Spring Boot REST API Layer            │
│   (Domain-Specific Microservices)           │
└──────────────────┬──────────────────────────┘
                   │
┌──────────────────▼──────────────────────────┐
│     Spring Data Neo4j ORM Layer             │
│   (Object-to-Graph Mapping)                 │
└──────────────────┬──────────────────────────┘
                   │
┌──────────────────▼──────────────────────────┐
│         Neo4j Graph Database                │
│   (Native Graph Storage & Processing)       │
└─────────────────────────────────────────────┘

🗄️ Graph Database Technologies

Choose the right database for your knowledge graph needs:

Database	Best For	Key Advantage	Use Case
Neo4j ⭐	General purpose, developers	Native graphs, Cypher, rich ecosystem	Fraud detection, recommendations
Oracle Graph	Enterprise RDBMS	Converged DB + graph with SQL/PGQ	Financial systems, compliance
RushDB	Rapid prototyping	Zero-config, auto-normalization	MVP, AI applications
TigerGraph	Large-scale analytics	Turing-complete GSQL, parallel processing	Graph algorithms, analytics
Gremlin/JanusGraph	Multi-backend systems	Universal traversal language	Portable graph solutions
Neptune, ArangoDB	Specialized needs	GraphQL, multi-model, serverless	Cloud-native applications

📦 Domain Modules

🏏 Cricket Analytics

Store and analyze cricket matches, players, teams, and performance statistics

Entities: Teams, Players, Matches, Performance Stats
Relationships: PLAYS_FOR, PLAYED_MATCH, WINNER, SCORED_IN
Use Cases: Player selection, team analysis, performance tracking

🏥 Healthcare Knowledge

Medical knowledge graph covering diseases, treatments, medications, and providers

Entities: Diseases, Treatments, Medications, Providers, Patients
Relationships: TREATS, HAS_SYMPTOM, PRESCRIBES, SPECIALIZES_IN
Use Cases: Drug interactions, diagnosis support, treatment recommendations

🧘 Yoga & Wellness

Yoga poses, practices, health benefits, and wellness knowledge

Entities: Poses, Practices, Benefits, Body Parts, Instructors
Relationships: TARGETS, BENEFITS, IMPROVES, PRACTICED_BY
Use Cases: Personalized yoga plans, injury prevention, wellness tracking

🛒 Supermarket & E-Commerce

Retail knowledge graph with departments, products, brands, and customer preferences

Entities: Products, Departments, Brands, Suppliers, Customers, Promotions
Relationships: IN_DEPARTMENT, MANUFACTURED_BY, PURCHASED_BY, ON_PROMOTION
Use Cases: Product recommendations, inventory management, customer insights

🚨 Fraud Detection

Fraud detection and prevention with types, methods, indicators, and prevention strategies

Entities: Fraud Types, Detection Methods, Indicators, Prevention Strategies
Relationships: DETECTS, INDICATES_FRAUD, PREVENTS, HAS_RISK_LEVEL
Use Cases: Fraud ring detection, anomaly detection, risk assessment

🐦 Bird Migration Ecology

Knowledge graph on bird species, migration patterns, habitats, and conservation

Entities: Bird Species, Migration Routes, Habitats, Seasons, Stopovers
Relationships: MIGRATES_VIA, HABITATS_IN, ENDANGERED_BY, MONITORED_BY
Use Cases: Conservation tracking, climate impact analysis, ecology research

🍽️ Recipe & Food Pairing ✨ NEW

Culinary knowledge graph with recipes, ingredients, cuisines, and dietary requirements

Entities: Recipes, Ingredients, Cuisines, Chefs, Dietary Requirements, Allergens
Relationships: USES_INGREDIENT, PART_OF_CUISINE, CREATED_BY, PAIRS_WITH
Use Cases: Menu planning, allergen warnings, recipe recommendations
Features: 100% vegetarian, ingredient substitutions, cuisine fusion

🎵 Music Recommendation Network ✨ NEW

Music knowledge graph with artists, songs, albums, genres, and playlists

Entities: Artists, Songs, Albums, Genres, Producers, Playlists, Users
Relationships: CREATES, FEATURES_IN, PRODUCED_BY, IN_GENRE, IN_PLAYLIST
Use Cases: Music recommendations, collaboration discovery, playlist curation
Features: Genre evolution, artist networks, personalized recommendations

🔥 Key Features

✨ Multi-Domain Support - 9+ ready-to-use knowledge graph templates 🔄 Real-Time Visualization - Interactive D3.js-powered graph exploration 🧠 Intelligent Querying - Cypher query language for complex relationships 🚀 Scalable Architecture - Spring Boot microservices pattern 🔐 Data Safety - Neo4j Aura cloud-hosted database 📊 Rich Analytics - Pattern detection, cycle detection, recommendations 🎨 Beautiful UI - Modern, responsive web interface with legend and controls ⚡ Fast Performance - Graph database optimized for relationship traversal

🚀 Quick Start

Prerequisites

Java 17+
Maven 3.8+
Neo4j Aura account (or local Neo4j instance)

Installation

Clone the Repository

git clone https://github.com/vishalmysore/VidyaAstra.git
cd VidyaAstra

Set Environment Variables ```bash
Windows

set NEO4J_PASSWORD=your-neo4j-password

Linux/macOS

export NEO4J_PASSWORD=”your-neo4j-password”

3. **Build the Project**
```bash
mvn clean install

Run a Module ```bash
Cricket Module

mvn spring-boot:run -Dspring.profiles.active=cricket

Healthcare Module

mvn spring-boot:run -Dspring.profiles.active=hc

Fraud Detection

mvn spring-boot:run -Dspring.profiles.active=fd

---

## 📚 Neo4j Cypher Queries

### Cricket Module Examples

**1️⃣ List all teams and their players**
```cypher
MATCH (t:Team)<-[r:PLAYS_FOR]-(p:Player)
RETURN t.name, collect(p.name) AS Players;

2️⃣ Teams ranked by wins

MATCH (m:Match)-[:WINNER]->(t:Team)
RETURN t.name AS Team, count(m) AS Wins
ORDER BY Wins DESC;

3️⃣ Players by role

MATCH (p:Player)
RETURN p.role, collect(p.name) AS Players;

4️⃣ Matches between specific teams

MATCH (m:Match)-[:TEAM_1|TEAM_2]->(t:Team {name: "India"})
RETURN m.matchDate, m.venue, m.result;

Fraud Detection Examples

1️⃣ Find fraud rings (cycles)

MATCH (n)-[r*]->(n)
WHERE length(r) > 1
RETURN n, r;

2️⃣ High-risk transactions

MATCH (f:FraudType)-[:HAS_INDICATOR]->(i:Indicator {riskScore: "High"})
RETURN f.name, i.pattern;

3️⃣ Detection method effectiveness

MATCH (d:DetectionMethod)-[:DETECTS]->(f:FraudType)
RETURN d.name, d.accuracy, count(f) AS FraudTypesDetected
ORDER BY d.accuracy DESC;

🏗️ Database Schema

Node Labels

Team, Player, Match (Cricket)
Disease, Medicine, Treatment (Healthcare)
Pose, Practice, Benefit (Yoga)
Product, Department, Brand (Supermarket)
FraudType, DetectionMethod, Indicator (Fraud Detection)
Recipe, Ingredient, Cuisine (Recipes)
Artist, Song, Album, Genre (Music)

Relationship Types

PLAYS_FOR, PLAYED_MATCH, WINNER (Cricket)
HAS_DIAGNOSIS, TREATS, PRESCRIBES (Healthcare)
TARGETS, BENEFITS, IMPROVES (Yoga)
USES_INGREDIENT, PART_OF_CUISINE, PAIRS_WITH (Recipes)
CREATES, FEATURES_IN, IN_GENRE (Music)

⚙️ Configuration

application.properties

spring.neo4j.uri=neo4j+s://your-instance.databases.neo4j.io
spring.neo4j.authentication.username=neo4j
spring.neo4j.authentication.password=${NEO4J_PASSWORD}
spring.neo4j.database=neo4j

🧪 Testing

Run the complete test suite:

mvn test

Test specific module:

mvn test -Dtest=CricketServiceTest
mvn test -Dtest=HealthcareServiceTest
mvn test -Dtest=CycleDetectionServiceTest

🐛 Troubleshooting

Issue	Solution
Neo4j Connection Failed	Verify NEO4J_PASSWORD is set and Neo4j instance is running
Build Fails	Clear cache: `mvn clean`, verify Java 17+, check internet connection
Graph Not Displaying	Check browser console for errors, verify all node references exist
Slow Queries	Add indexes to frequently queried properties in Neo4j

📈 Performance Optimization

Neo4j Best Practices

-- Create indexes for frequently queried properties
CREATE INDEX ON :Player(name);
CREATE INDEX ON :Team(name);
CREATE INDEX ON :Disease(name);

-- Use EXPLAIN to analyze query performance
EXPLAIN MATCH (p:Player)-[:PLAYS_FOR]->(t:Team) RETURN p, t;

🤝 Contributing

We welcome contributions! Here’s how:

Fork the repository
Create a feature branch (git checkout -b feature/amazing-feature)
Commit your changes (git commit -m 'Add amazing feature')
Push to the branch (git push origin feature/amazing-feature)
Open a Pull Request

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

👨‍💻 Author

Vishal Mysore
Knowledge Graph Architect & Graph Database Enthusiast

GitHub: @vishalmysore
Project: Vidya Astra on GitHub

🙏 Acknowledgments

Neo4j - For the amazing graph database platform
Spring Boot - For the powerful framework
D3.js - For incredible data visualization
Open Source Community - For endless inspiration

📞 Support

📖 Documentation: Check the /docs folder
🐛 Issues: Report bugs on GitHub Issues
💬 Discussions: Join our community discussions
📧 Email: Contact for enterprise support

### ⭐ If Vidya Astra helps you, please star the repository! **"Knowledge is Power. Graph Databases are Faster."** 🚀

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