I'v been writing my own graph database which uses GPGPU to do querying. Since graphs are a superset of relational databases, I'm planning using it to power most of my future projects.
I added predictive modeling features to it as well. There's an algorithm i'm working on which will dynamically create/prune edges between verticies and represent them as recurrent neural networks or baysian networks (depending on which is more applicable) and attempt to find the relationtion between the data based on known patterns and time-sequence data.
Preliminary testing has shown it can reduce the total amount of needed edges in the network and find previously unknown relationships between verticies. Although for mostly uncorrelated data it doesn't work at all.
Its as of yet unnamed and unpublished, but I plan to release open source it once it is stable, if anyone else is interested.