JanusGraph

Ingest transaction data into JanusGraph via Spark Streaming.

Trigger a Gremlin traversal on each new transaction.

Check for deep-nested relationships between sender and known fraudulent entities.

Flag transactions if a path length < 4 is found to a blacklisted node.

Load raw customer data from CRM and Web Logs.

Apply fuzzy matching logic to create 'SAME_AS' edges between nodes.

Use JanusGraph traversal to find the 'Golden Record' vertex.

Query the unified graph for marketing personalization.

Map every server, container, and service as a vertex.

Create edges for 'DEPENDS_ON' and 'RUNS_ON' relationships.

Run impact analysis traversals when a specific node fails.

Identify single points of failure using graph centrality algorithms.

Ingest firewall and system logs into the graph.

Query for lateral movement patterns (hop-by-hop traversals).

Visualize the attack vector from initial entry to data egress.

Automate the detection of unusual access patterns via graph shape analysis.

Store users and products as vertices.

Store 'PURCHASED' and 'VIEWED' as edges with weight properties.

Use Gremlin to find 'Customers who bought X also bought Y' in real-time.

Rank results by edge weight and frequency.

Model the supply chain as a directed acyclic graph (DAG).

Update node status (e.g., delay at port) in real-time.

Run shortest-path algorithms to find alternative routes.

Calculate the ripple effect of a component shortage.

Extract entities and relations from unstructured text.

Populate JanusGraph with these triples.

On user query, perform a sub-graph extraction for context.

Feed the structured graph context into the LLM prompt.