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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 can do much better than this when complex causation is involved. Charting relationships qualitatively and quantitatively, as a graph (the mathematical name for a network), may offer a more natural and complete way of representing processes. The graph format has the additional benefit of visually mapping to the trajectories of unfolding processes (such as fluid motion, state transitions, or even logical flow charts).
Types are not enough…
I recall an episode from many years ago, during the early development of Promise Theory, in which a PhD graduate from the University of Oslo came to discuss my approach and tell me about his problem. The student had worked on Object Oriented Type-Analysis for his thesis and had been unable to resolve a final issue. His advisor had identified the case as a paradoxical problem in OO modelling, and they had spent some years working on it. After hearing about the problem, I sketched it on the blackboard, using Promise Theory, and showed him how I would have done it. His mouth fell open as he saw that the answer was so obvious — a simple shift in thought, from the imposed obligation of a rigid type model to autonomous promises, made the issue unambiguous. Freed of a restraining doctrine, we had solved the problem immediately. It was one of many incidents that pushed me towards network modelling without the artificial logical structures that are tradition in mathematics — towards ideas like Semantic Spacetime.
I share this story because we sometimes imagine that modelling choices are easy, and that analysis will be more or less the same no matter what static data model we choose. Any model is surely as good as any other — it’s just a matter of style, right? But this is not so. Sometimes we set ourselves up for success or failure by the choices we make at the start. That’s also true about the choice of tools. Happily, Go with ArangoDB, have proven to be the clearest and most efficient version of these ideas I’ve been able to produce so far!
In this fifth post of the series, I want to show how we can represent completely general semantic relationships, between events in a process, using the semantic spacetime tools and a graph representation. Using Go and ArangoDB, this has never been easier, but you might have to tune your thinking to a different frequency to avoid some pitfalls.
In computer coding, the concept of data types is by now quite ubiquitous and is used with a specific purpose in mind: to constrain programmers, by being intentionally inflexible! Data types, like double-entry book-keeping in accounts or categories, are designed to highlight errors of reasoning by forcing a discipline onto users. Types have to match in all transactions, else syntax error!