Predictive Maintenance with Neo4j Aura Graph Analytics for Factory Uptime

In this technical walkthrough, we explore how Neo4j Aura Graph Analytics can be used to implement predictive maintenance strategies in manufacturing environments—avoiding costly machine failures and production downtime.
Learn how graph models outperform traditional relational databases when identifying:
– Machines most likely to fail
– Critical nodes on production paths
– Alternative routing when a machine goes down

We cover core graph algorithms including:
– Betweenness Centrality for finding hidden bottlenecks
– PageRank to identify key hubs
– Strongly Connected Components for detecting loops
– FastRP Embeddings + KNN for structural similarity analysis

🚀 This session features a full demo using Neo4j Aura Graph Analytics, including:
– Creating in-memory graph projections
– Running GDS algorithms
– Writing results back to AuraDB for real-time insights

Whether you’re building a smart factory dashboard, exploring graph ML, or want a deeper look at real-world graph applications—this is for you.

Perfect for:
Developers, Data scientists, ML engineers, backend devs, and architects working in analytics, manufacturing, or IIoT systems.

#GraphAnalytics #Neo4j #PredictiveMaintenance #AuraDB #MachineLearning #GDS #FactoryOps #DataScience #GraphAlgorithms

Explore Neo4j Aura Graph Analytics → https://bit.ly/4mCQKQQ
Learn more about Neo4j AuraDB → https://bit.ly/451ssda
Access the code and dataset on GitHub → https://bit.ly/3FvQuCC