by Breck Yunits
All tabular knowledge can be stored in a single long plain text file.
The only syntax characters needed are spaces and newlines.
This has many advantages over existing binary storage formats.
Using the method below, a very long scroll could be made containing all tabular scientific knowledge in a computable form.
There are four concepts to understand:
First we create measures by writing parsers. The parser contains information about the measure.
The only required information for a measure is an id, such as temperature
.
An example measure:
temperatureParser
Next we create concepts by writing measurements.
The only required measurement for a concept is an id. A line that starts with an id measurement is the start of a new concept.
A measurement is a single line of text with the measure id, a space, and then the measurement value.
Multiple sequential lines of measurements form a concept.
An example concept:
id Earth
temperature 14
Unlimited comments can be attached under any measurement using the indentation trick.
An example comment:
temperature 14
> The global mean surface air temperature for that period was 14°C (57°F), with an uncertainty of several tenths of a degree.
- NASA
https://earthobservatory.nasa.gov/world-of-change/global-temperatures
Putting this all together, all tabular knowledge can be stored in a single plain text file using this pattern:
idParser
temperatureParser
id Earth
temperature 14
> The global mean surface air temperature for that period was 14°C (57°F), with an uncertainty of several tenths of a degree.
- NASA
https://earthobservatory.nasa.gov/world-of-change/global-temperatures
Once your knowledge is stored in this format, it is ready to be read—and written—by humans, traditional software, and artificial neural networks, to power understanding and decision making.
Edit history can be tracked by git.
Modern databases[1] were designed before git[2], fast filesystems[3], and the Scroll stack[4], all requirements of this system.
GNU Recutils[5] deserves credit as the closest precursor to our system. If Recutils were to adopt some designs from our system it would be capable of supporting larger databases.
ScrollSets is the name of the first implementation of the system above. It is open source and dedicated to the public domain.
ScrollSets are used to power the open source website PLDB.io. PLDB currently has over 300 measures, over 4,000 concepts and over 150,000 measurements, contributed by over 100 people, dozens of software crawlers, and a couple of artificial neural networks.
If printed on a single scroll, the PLDB ScrollSet would be over one kilometer long.
sortIndex
attribute on your measures to automatically prioritize[8] the measurements in your source and output files. The impact of this simple enhancement hints at interesting signs of dense information packing achieved by this method, which may have implications for the weights and training of artificial neural networks.minParser
under temperatureParser
to generate a temperature_min
column name in a generated TSV.ids
.Measurements loosely map to nucleotides; concepts to genes; parsers to ribosomes.
This system might also have broad use.
You can read more about ScrollSets on the Scroll blog, see small demos at sets.scroll.pub, and see the large implementation at PLDB.io.
[1] SQL: Donald D. Chamberlin and Raymond F. Boyce
[2] Git: Linus Torvalds, Junio Hamano, et al
[4] Particles: Breck Yunits et al (formerly called Tree Notation)
[5] GNU Recutils: Jose E. Marchesi
[7] Racket: Matthias Felleisen, Matthew Flatt, Robert Bruce Findler, Shriram Krishnamurthi, et al.
Thank you to everyone who helped me evolve this idea into its simplest form, including but not limited to, A, Alex, Andy, Ben, Brian, C, Culi, Dan, G, Greg, Jack, Jeff, John, L, Liam, Hari, Hassam, Jose, Matthieu, Ned, Nick, Nikolai, Pavel, Steph, Tom, Zach, Zohaib.