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Federated Data Interpretation Systems Reference Architecture (FDIS-RA)

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Discussion

The Federated Data Interpretation Systems Reference Architecture (FDIS-RA) defines a common architectural framework for systems that interpret, validate, compare, and govern data produced through statutory, regulatory, administrative, or other governed reporting processes.

FDIS-RA addresses environments in which multiple organizations, agencies, jurisdictions, systems, or implementations participate in the production, extraction, interpretation, validation, or use of reported information. The architecture treats federation as a condition in which responsibilities and authority may remain distributed while systems coordinate through governed architectural boundaries.

FDIS-RA defines architectural responsibilities for:

FDIS-RA separates operational processing within the data plane from governance, configuration, semantic authority, and lifecycle control within the control plane. It defines explicit components, ports, port interfaces, feeds, streams, stream elements, adapters, responsibilities, and governance mechanisms at the platform-independent level.

The Financial Data Transparency Act (FDTA) provides a representative motivating case. FDIS-RA does not derive its applicability solely from the FDTA and does not replace agency-specific rules, reporting requirements, enforcement authority, or legal judgments.

FDIS-RA serves as the proposed response to the Structured Information Processing Reference Architecture (SIP-RA) Request for Proposal. It applies the architectural separation of data, structure, semantics, and interpretation required by SIP-RA to federated data interpretation environments.

Definition

Platform-independent Reference Architecture defining governed responsibilities, boundaries, interfaces, and lifecycle controls for federated interpretation of regulated data.

Source

Note

Example

A regulator may apply FDIS-RA when acquiring an environment that preserves source filings, extracts observations with provenance, interprets those observations under versioned semantic definitions and rules, validates the results, and exposes traceable outcomes across governed interfaces.

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