I think that you are you are on a solid research path here. I think you have reached the bounds of business oriented software and it’s time to look into something like apache mahout or RDF. Decision tree implementations are available all over, just find a data structure and share them and run inference engines like owlim or pellet and see what you can see.
RDF is a good interim solution because you can start encoding things as structured data. I have some JSON->RDF stuff for inference if you get to that point.
Here is one way to represent these graphs as RDF.
Each edge becomes an edge to a blank node, that blank node has the label, arrival probability and could link to evidence supporting. Representing weighted graphs in RDF is fairly well studied.
The question is, what is your net goal of this from a computational artifact point of view?
I think that you are you are on a solid research path here. I think you have reached the bounds of business oriented software and it’s time to look into something like apache mahout or RDF. Decision tree implementations are available all over, just find a data structure and share them and run inference engines like owlim or pellet and see what you can see.
RDF is a good interim solution because you can start encoding things as structured data. I have some JSON->RDF stuff for inference if you get to that point.
Here is one way to represent these graphs as RDF.
Each edge becomes an edge to a blank node, that blank node has the label, arrival probability and could link to evidence supporting. Representing weighted graphs in RDF is fairly well studied.
The question is, what is your net goal of this from a computational artifact point of view?