Part 4. Keys, patterns, maps, and learning — the beginnings of representational semantics in data
Imagine what happens when you walk into a smart building, wielding your biometrics and wearing digital electronic devices — recording your environment with embedded sensors, smart glasses or pads. A torrent of information interactions and registrations is unleashed, leading to a virtual “wiring up” of devices with services for identification and authentication. It’s a lot of information, which could be represented by flowcharts, websites, data circuitry, supply chains, user logs, cartography, etc — in all cases, there are complex interactions between key-valuelook-ups, document archives, scratch-pads used by algorithms, and graphical maps of events play a basic role in both what we experience and create. In this post, I’ll explain (hands-on) the mechanics of representing spacetime processes as associative memory, both in Go code and in the ArangoDB database.
Space and time underlie all data
Pattern is the foundation of all information, and patterns need space. A pattern can be a simple bit (on or off), a complex string, one or more documents with structured fields, or a complex web of nodes expressing intricate relationships — and all changing in time. Without some kind of notion of space, there couldn’t be information. Space is what we use for memory. It’s the fabric of state.
We probably tend to think of space as the kind of open space we learn in school (Euclidean space), but there are other kinds too. If we ignore reserved mathematical terminology, then space is just a collection of distinct locations, without any assumed structure: a single bit (a light bulb), a row of buckets, a spider’s web, or an infinite chess board, are all versions of space. They contain degrees of freedom that can hold and express information.
Time, on the other hand, is the phenomenon of state changing, expressed at these locations. Changes to that state are known as a process’s proper time. So space and time are not independent things. Together, they describe processes (or spacetime phenomena) — and we strive to observe, understand, and evenintentionally manipulate these processes for a purpose. This purpose is what we refer to by semantics.
The options for representing space in IT begin with the smallest atoms: bits, from there words, and we are already into the realm of key-value stores. From associating names with value, we can form arrays of them, more structured packages like “documents” or “tables” (vectors and tuples), and finally graphsor networks with completely ad hoc structures. I’ll work through these in the languages of the chosen tools, starting with the basic atomic building blocks.
Space is memory, and memory means “variables”. Key-value associations were called associative arrays in the heyday of