I am looking for suggestions on creating a temporal nested data set model. I am trying to improve performance for reading sections. I have a node tree of ~1million nodes, with frequent depths of 20+ nodes. The tree stores categories that can change over time, with the ability to enter future changes.
The current data structure is a temporal adjacent node model, modelling changes to the node tree over time is trivial with a simple data structure:
Nodes
nodeID
[data]Edges
parentNodeId
childNodeId
validFromDate
validToDate
A nested data set makes for very fast read operations, but my current understanding of nested sets does not support changes over time to the tree
Nodes
nodeId
left
right
[data]
One thought I had was to create a series of "nesting maps" which reflect the left/right values at given points in time, but this would mean recreating the entire node tree whenever a single change-over-time was modelled, which would make the size of the "Nests" dataset too large as changes are frequent.
Nests
nodeId
left
right
validFromDate
validToDate
Has anyone created a temporal nested dataset model, or know any good resources on the subject?
