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Universal Data Analytics as Semantic Spacetime(Part 8)

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 multiple scales. I’ll use a couple of more realistic examples that show how these real-world processes form graphs: i) with some Natural Language Processing based on fragmentation of a stream of input, analogous to bioinformatics and ii) multi-path flow processes with alternative routes, such as we find in routing, supply chains, and quantum experiments. We’ll see how the coding of a data representation in applications can now be extremely simple, and how the four spacetime relations allow us to see and explore beyond constrained typological relationships, to reveal emergent discoveries about the data.

Graphs are active not passive structures

In part 7, I showed how to capture and generate spacetime process graphs, merging parallel timelines where necessary. At the data capture stage, we may be entirely unaware of the existence of a graph representation— only later, during analysis, would the graph be revealed as a network. Identifying a working representation early will be an advantage, but we mustn’t over-constrain a representation and kill off the benefits of a network in the process. Graphs naturally want to remain active processes, not become passive or dead archival data structures. In a sense, the construction and the interaction by search are continuations of the same larger cognitive process. The semantic spacetime co-activation method generates this for us without prejudice.

In this post, we’ll look at how to create and search active graphs and explain the meaning of their large scale structures as they grow. It turns out that searching graphs is the more interesting problem, because it exposes an apparent conflict between the desire for a single-threaded storytelling about data, and the multiplicity of causal chains in the larger spacetime that might ultimately contribute to an outcome. This is the “pipeline” problem. Unlike typological approaches (e.g. OWL, RDF, etc) that aim for uniqueness and rigidity of reasoning, a spacetime approach works with general superpositions of concerns that emerge unpredictably from complexity at scale.

There are two main themes to address in this post:

  • Causal path relationships as processes (timelike trajectories), which may include loops. These are naturally represented by graphs.

  • Locally expressed attributes and similarities (snapshots in space), which describe intrinsic nature (scalar promises). These may be represented either as gra