Part 1: New Horizons for Sensory Learning Every volume of space, each snapshot in time, embodies a microcosm of information bursting with meaning. What’s it all for? In our modern world of smart technology, information can be offered to us in the form of services that are engineered for human…

Part 2: Setting up the Tools I confess to having a love and hate relationship with tech. In an ideal world, one could skip this post, because software would be easy to get started with, and the answers to simple questions would be easy to find — but that’s not…

Part 3: The basics of space and time, and the most familiar multi-model databases you never knew… In case you hadn’t noticed, there’s a very important multi-model database hidden in plain sight, right under our noses. We might never think about it in those terms, but we rely on it…

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…

Part 5. Graph spacetime, association types, and causal semantics Data analysis inevitably involves relationships between sources and the signals they give rise to — with, of course, their associated semantics. So far we looked at only simple tabular associations (so-called documents), typical of what’s usually stored in databases. But we…

Part 6. Applying reusable SST patterns for graph data modelling With the basic tools and conceptual issues in place, we can begin to apply them more seriously to build database-backed models of the world, including (machine) learning structures. The purpose of storing data is then not just to build an…

Part 7. Coincidence and the Four Horsemen of Semantics After taming the tools in foregoing episodes, we now have a robust set of techniques for comprehending and organizing data, i.e. for turning input streams into analytical structures. These dynamical “knowledge representations” describe information flows or spacetime processes on all levels…

Part 8: The Cone of Semantic Future: Causal Prediction, Entanglement, and Mixed States in Graph Spacetime Having developed some of the basic code patterns, and introduced the four spacetime relations, let’s explain how these four semantic interpretations can be used in practice — to understand and predict dynamical processes across…

Part 9: Graph structure, process computation, and dimensional reduction There’s surely an uncountable number of atoms in a beam holding up your roof, and it’s clear that we don’t need to refer to each atom to talk about the role of the beam. The beam is a superagent; it has…

Part 10: Big data, coarse graining, and “effective” approximations — In the previous post, I looked at how large scale properties of graphs could be calculated from smaller scales, and how the graph itself reveals the computation! Before we end this series of posts, we need to finish addressing the elephant in the room, which is scale, in all its…

Part 11: In search of answers, beyond this horizon — In this series, I’ve discussed at length — and with explicit examples — what we can do with the principles of Semantic Spacetime to make sense of generalized data analytics, using just a couple of tools on a personal computer. For some applications, indeed for exploring and learning, this combination…

Appendix 1: Exploring the digital barrier reef with CAIDA and ArangoDB We probably think we know the Internet, but how many can really say they understand how it works? What do you think of when you hear the word? Your favourite websites? Social media? Perhaps the WiFi router supplied by…

As we expand the scale of our highly networked “cloud” computing across the globe (and someday even beyond), we rediscover that the issues of computing are in fact issues of space and time, just as they are in all physical and virtual systems. …