9.8 Cross-Concern Relationships

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The concerns in this model interact, but they remain distinguishable.

For example, a Data Structure Definition combines Structure with references to Semantic meaning. A validation process may Interpret Data using Semantic definitions and Governance and Authority-approved rules. A release decision may depend on Data values, Semantic classification, policy authority, and Evidence requirements. A provenance record may connect Data, Interpretation, Governance and Authority, and Evidence into a single reviewable chain.

To identify a cross-concern relationship, record:

Concerns involvedElement, artifact, rule, or decision creating the relationshipGovernance and Authority mechanism controlling the relationship, where applicableTraceability relationship preserving the connectionEvidence supporting later review, where applicableTable 9.8-1 provides examples of cross-concern relationships.

Table 9.8-1: Examples of cross-concern relationships in structured information processing

Originating concern	Related concern	Example
Structure	Semantics	A field definition references the governed meaning of a currency code.
Semantics	Interpretation	A semantic classification provides context for a validation rule.
Interpretation	Evidence	A derived analytical assertion records the rule version and input Data used to produce it.
Governance and Authority	Semantics	A semantic authority approves a change to a definition or classification.
Governance and Authority	Traceability and Evidence	A release policy requires Evidence of the release decision and handling obligations.
Data	Traceability and Evidence	A persisted transaction record links to provenance and audit records.

Cross-concern relationships support a governable architecture. They allow concerns to interact without allowing one concern to absorb or obscure another.

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