a plain text file is the oldest idea for storing knowledge. see unix philosophy: "Write programs that do one thing and do it well. Write programs to work together. Write programs to handle text streams, because that is a universal interface."
If you take out plain text from this presentation, what's left? The tree structure? The log aspect? In order to claim any of this is remotely novel, you have to first ignore the whole body of work built around information systems.
> If you take out plain text from this presentation, what's left? The tree structure? The log aspect? In order to claim any of this is remotely novel, you have to first ignore the whole body of work built around information systems.
Thank you for the feedback. I've updated the paper with some more links.
The language in which the measures are written in (currently called Grammar. I will like rename it to something like Parssers) is quite advanced.
The improvements over Recutils, the closest precursor I am aware of, have now been added.
The PLDB ScrollSet is now about 500,000 cells of information. Each cell is strongly typed and fully auditable by git. There is a high amount of signal in that dataset. It is an intelligent set of weights, and continually getting more intelligent. And it is read at runtime as a single plain text file and compiled to a single CSV (or tsv, json, etc).
All from using the system documented in the paper (and the advanced language for Parsers).
If you can point me to a similar database or similar scale anywhere in the world (plain text base, >10e5 size, git backed, strongly typed, hierarchical and graphical), I would be grateful as I might learn something.
