The greatest thing about relational databases is they store everything loose in some kind of homogeneous level playing field. It is only be establishing relationships between data that anyone is able to see anything in context. Without context, they are just data. In context they are messages, thoughts, ideas, studies, results, and work products.
If an idea is very complex sometimes it helps to break it down into component parts. Systematically taking it apart to understand what makes this idea tick.
Taking an idea apart can be very informative. Especially when various parts need to be updated and optimized, continually changing like software releases. If the watch above was wordpress, the Swift theme, and the internet each gear changes sooner or later but the whole watch still needs to work together if it is to continue functioning. Putting things back together offers it’s own set of challenges. There is an opportunity to purge elements that are no longer useful during this process. Like a hoarder moving everything out of their house onto the curb then back into the house, maybe some of those items are not worth saving after all. Or fixing a car engine, or someones medical condition, when it is unclear exactly what the problem is but simply by taking it apart and putting it back together, whatever was not working gets repaired.
Instructions are needed, parts need to be labeled. A sequence of reassembly is needed to ensure the reassembled whole still is the same. It can be difficult to see how the parts fit together when viewed too close.
Because everyone’s perception and experience is different, the exact same elements, in almost exactly the same combination may be understood a different way from different points of view. The receiving end may be “reading something into” what the sender intended. It may not be possible for two different people to consistently see the same things the same ways.
However, this is not true for machines like computers or networks like the internet because machines have no prejudices, emotions, or previous experiences. They simply process the information, break up whole ideas into packets, send them somewhere, another machine puts them back together. For this to be reliable everything on both ends needs to be a repeatable process. It would be so helpful to have a mold with the end result packed in with every packet to ensure consistency. MIT has just started a project to map controversies that may be useful to understand multiple interpretations of the same information.
This project is important today because we are surrounded by so many controversies, and so much data, it’s difficult to sort out which parts are actually valid, worth processing, keeping in the information houses where we store things. For example the Washington Post had an article today about the disconnect between science and the general public entitled “Not Blinded by Science, but Ideology” where global warming is a perfect example.
To avoid using information the wrong way, or putting together messages, thoughts, and ideas that may be different than original authors intended, especially while processing the data in emotionless machines – repeatable processes are needed.
Today the primary representation of how pieces of information are to be put back together need to work with SQL. Looking at the relationships is usually just miles and miles of code. However, there is a company at http://mkweb.bcgsc.ca who makes Schemaball, a Schema Viewer for SQL Databases where the relationships themselves can be put under a microscope and examined across the whole database in one glance.
It’s curious why geometry proper is not used more often to direct the arc, layouts and relationships. Something like a mold could be useful to ensure the reassembly is 100 percent correct on the receiving end, to match exactly, what the sender intended.
But how would you store and encode that geometry?