Graph databases allow users to analyze highly interconnected datasets and find patterns within these relationships. Social networks, corporate hierarchies, fraud detection, network analytics, or building whole knowledge graphs are great use cases for graph databases. However, these datasets of nodes and connecting edges change over time. Whether you are a developer, architect or data scientist, you may want to time travel for analyzing the past or even predict tomorrow.
While your graph database may be lacking built-in support for managing the revision history of graph data, this talk will show you how to manage it in a performant manner for general classes of graphs. Best of all, this won't require any groundbreaking new ideas. We'll simply borrow a few tools and tricks from existing persistent data structure literature and adapt them for good performance within the graph database software.