RMS-EMMA / NUE 112

A computational Data Platform must often perform an operation that appears paradoxical: it dismantles source records in order to preserve information more faithfully. The source is decomposed into identities, events, attributes, relations, temporal anchors, and provenance, then recomposed as stable Data Objects and fit-for-purpose views. This essay formalises that operation as a sequence of projections and constrained joins. It distinguishes decomposition from destructive normalisation and reconstruction from the naïve reassembly of source tables. The broader cultural argument is that information architecture resembles critical interpretation: a received text is analysed into structures and variants before a responsible edition is produced. The platform should preserve the source as evidence while refusing to inherit its accidental organisation. The result is an architecture in which new sources can be absorbed and new views produced without repeatedly rebuilding the semantic foundations.

Lineage diagrams often show that a source contributes to a dataset or that a process touches a Data Object. Such maps are valuable, but they do not yet explain how a particular output attribute was computed, which predicate filtered a record, which reference version supplied a code, or which policy authorised publication. This essay develops a layered model of lineage from conceptual relevance to executable derivation. It formalises lineage as a typed, versioned, temporal provenance graph; distinguishes data, control, semantic, policy, and decision dependencies; and examines the conditions under which lineage can support impact analysis, replay, and explanation. The argument is that lineage becomes operational only when it can participate in computation. A picture documents architecture; executable lineage constrains and directs it.