ArcadeDB Use Cases

A collection of self-contained projects demonstrating how to use ArcadeDB.

Each use case lives in its own directory with a Docker Compose file, SQL schema/data files, and runnable demos via both curl and a Java program.

Use Cases

Directory	Description	ArcadeDB features
recommendation-engine	Intelligent product and content recommendations	Graph traversal, Vector similarity, Time-series
knowledge-graphs	Academic research knowledge graph with co-authorship and citation networks	Graph traversal, Vector similarity, Full-text search, Time-series
graph-rag	Graph RAG system combining knowledge graphs with vector search for retrieval-augmented generation	Graph traversal, Vector similarity, Full-text indexing, Neo4j Bolt, LangChain4j
fraud-detection	Fraud detection system unifying graph, vector, and time-series signals	Graph traversal, Vector similarity, Time-series, Cypher
realtime-analytics	Unified IoT and service monitoring platform	Time-series, Graph traversal, Cypher
social-network-analytics	Social network analytics with materialized view dashboards	Materialized views, Graph traversal, Time-series, Polyglot (SQL + OpenCypher)
supply-chain	Supply chain management with multi-tier visibility	Graph traversal, Vector similarity, Time-series, PostgreSQL protocol, JavaScript

Structure

Each use case directory contains:

• docker-compose.yml — ArcadeDB instance (pinned version)
• setup.sh — creates the database and loads schema + data
• sql/01-schema.sql — vertex/edge type definitions
• sql/02-data.sql — sample data
• queries/queries.sh — all queries via curl
• java/ — standalone Maven project running the same queries via Java
• README.md — quickstart guide