The two rectangles at the top are what I've got now: completed algorithms with demonstrated performance on reasonable test sets. That seems like a good start. So, how to tie them together and pull in enough bona-fide research to make it a dissertation?
My thought is this can evolve into the lower rectangle, which is basically a framework for performing multi-dimensional analysis on huge data sets in real time. The multi-dimensional part is what I've been missing up until now and it's the glue that holds all this together. I didn't really understand that until I put together my term presentation for D-BESt. Generalizing this to relational databases will kill a lot of the optimizations. Restricting the scope greatly increases the potential gains and also moves the research into a bit more of a niche (less likely to get scooped).
The fact that it's a niche, doesn't mean it doesn't have very real value. There are still lots of applications for multi-dimensional analysis. And, multi-dim datasets are growing much faster than existing tools can keep up with.
To get to the lower rectangle will require adding some important features, which are indicated by the diamonds. I thought about drawing arrows, but I don't know that I'll necessarily do them in the most logical order, which would be top to bottom. I'll probably implement them in the order I come up with decent ideas.
As for the ideas, the rounded boxes indicate related topics I can draw from. Again, I considered arrows, showing what related to what, but it got really busy. In general, the closer a rounded box is to a rectangle or diamond, the more relevant it is.
I'm figuring this creates a seed group of around 100 papers, which should be a good start. I'm sure I can't fully digest that many papers over the summer, but I should be able to at least locate them and get the major points into a survey. I'll revisit the details as I move through the actual research.
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