Adjustable, versioned graph database

Question

I\'m currently working on a project where I use natural language processing to extract emotions from text to correlate them with contextual information.

Definition o

Answer 1

You have an interesting project, I do not work on things like this directly but for my 2 cents -

It seems to me your picture is a bit flawed. You are trying to represent a graph database overtime but there isn't really a way to represent time this way. If we examine the image, you have conversations and context data changing over time, but the fact of "Bob" and "Alice" and "Malory" actually doesn't change over time. So lets remove them from the equation.

Instead focus on the things you can model over time, a conversation, a context, a location. These things will change as new data comes in. These objects are an excellent candidate for an event sourced model. In your app, the conversation would be modeled as a series of individual events which your aggregate would use and combine and factor to generate a final state which would be your 'relief' determination.

For example you could write logic where if a conversation was angry then a very happy event came in then the subject is now feeling relief.

What I would do is model these conversation states in your graph db connected to your 'Fact' objects "Bob", "Alice", etc. And a query such as 'What is alice feeling right now?' would be a graph traversal through your conversation states factoring in the context data connected to alice.

To answer a question such as 'What was alice feeling 5 minutes ago?' you would take all the event streams for the conversations and rewind them to the appropriate point then examine the state of the conversations.

TLDR: Separate the time dependent variables from the time independent variables and use event sourcing to model time.