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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 will take you quite far. It’s not necessary to jump into the deep end of High Performance Computing, Big Data, or Deep Learning to find answers. Some problems are certainly larger though: the long tail of the data processing power-law has plenty of hurdles and challenges, and I’ll return to these at a later date. In this final installment I want to summarize what we’ve accomplished using these basics.
Graphs want to be “alive”
In the series, I’ve tried to show how graphs are the natural data representation for active processes. Graphs are built by processes, they lay out the circuitry of flow processes, and interacting with graphs is an on-going process, not a random access story. Graphs are not merely frozen archives of passive data, graph circuitry remains an active spatio-temporal mirror of the world, with embedded directionality, and unresolved inline choices that capture complex causality. Every graph is a computer as well as a model of state. Thus graphs are samples of spacetime — with semantics.
Today, we have a battery of methods developed to calculate certain properties of unlabelled graphs, at least when their nodes are homogeneous and memoryless. We sum up weighted contributions, e.g. in Artificial Neural Networks, or “entire” Graph Algorithms such as eigenvector centrality (PageRank) etc, to expose certain structures just from the topology. But the most fundamental resource for machine learning and causal computation lies in the variable data held within graph: vertices or nodes, and their labelled edges or links. Advanced machine learning is accomplished by memory processes with strong semantics, by traversing both symbolic and quantitative information.
Graphs resist all attempts to be frozen into static snapshots or dead archives. Standardized (one might even say “authoritarian”) hierarchies, like taxonomiesor ontologies, tables of contents, etc. try to define spaces, like a fixed coordinate system in Euclidean space. These are common but fail to capture the nuances of real world complexity — because these coordinate systems are only ad hocspanning trees, i.e. incidental route maps overlaid onto a snapshot of an evolving system. As we learned from Einstein, relativity of viewpoint and circumstance forces us to change perspective. Every spanning tree yields a unique “view” or partitioning of data, but usually the deeper invariant semantics within nodes don’t fall into these ad hoc treelike hierarchies. This is why hierarchical filesystems still need symbolic links to patch them up into a usable state.
The “liveness” of network data makes graphs a key part of the animated storytelling of the world around us — from embedded edge computing, within local ecosystems, to the routing of signals and resources for control purposes. This characteristic also makes graph databases quite different (i.e. highly non-linear) compared to their static SQL cousins. The process of querying, and working with, graph data for the realtime evolution of a syste