SpatioTemporal

The ability to produce 'sense' as a foundational AI framework, requires a great deal of work to figure out how to represent and process millions of data-points that is likely to scale into a fabric that involves more than trillions of graph statements.

In-order to figure out the method, the considered approach is to define a methodology based upon the production of a particular story line, or 'songline'. As such, the one that's been elected is the history and storyline of how digital identity and the recent global events have some form of meaningful relationship, that the project will thereby seek to better illustrate. This example, is in-turn far simplier than the underlying purpose / requirement, for establishing a fabric that is thereby able to be defined as 'fit for purpose' for language; which in-turn, leads to advancement of the sense project more broadly.

Existing solutions

This is an example of an existing SpatioTemporal big datastore.

This example is not considered to be either fit for purpose, nor decentralised. However it does provide a great deal of insights about the GIS related functionality that is desirably supported.

The TheTimelinesProject provides some basic information about an existing method that can be used more broadly, however the purpose of the DIDSSICovidSonglines is to significantly advance the capabilities via this complex example; that is in-turn a radical simplification of what is otherwise more broadly required for the sense-project more broadly.