GEARA 0.1 - Generic Enterprise Analytics Reference Architecture

Attribute	Value
Specification	Generic Enterprise Analytics Reference Architecture
Acronym	GEARA
Version	0.1
Status	Normative Draft / Executable Release Candidate
Specification Type	Generic Reference Architecture
Scope	Governed Enterprise Analytics Architecture
Semantic Foundation	GEARM 0.1
Data Semantic Foundation	GEDRM 0.1
Product Semantic Foundation	BPS / PIN and applicable Product-family specifications
AI Product Semantic Authority	AIPS
Primary Data Architecture Companion	GEDRA
Normative Architectural Authority	This human-readable GEARA specification

Specification Status

GEARA 0.1 establishes the initial normative conceptual baseline for the Generic Enterprise Analytics Reference Architecture. It defines the logical responsibility model, cross-cutting architectural fabrics, interactions, capabilities, Architecture Building Blocks, contracts, registries, governed metadata, governance and control architecture, security/privacy/trust architecture, lifecycle and resilience architecture, conformance model, architectural invariants, reference views, and canonical architectural patterns required to support governed enterprise analytics.

GEARA 0.1 is the architectural counterpart of GEARM 0.1. GEARM defines the generic semantic model for enterprise analytics; GEARA defines how architectural responsibility for realizing, governing, operating, consuming, assuring, and evolving those semantics is organized.

The GEARA 0.1 machine-readable artifact programme is now closed through GEARA-A15 as GEARA-0.1-RC1, an Executable Release Candidate. GEARA-A1 through GEARA-A14 establish the canonical architecture registries, invariant catalogue, conservative RDF/OWL and SHACL formalization, JSON-LD and JSON Schema representations, governed Profile schema, canonical examples, positive and negative testcases, automated conformance runner, cross-artifact reconciliation, and governed regression baseline. GEARA-A15 freezes the complete A1-A14 release subject through a manifest and SHA-256 inventory, records explicit known limitations and promotion policy, and requires the established machine gates to remain satisfied before the candidate is considered closed. Promotion from Executable Release Candidate to final Released status remains a separate governed publication decision.

Publication note: GEARA is published within the BPS specification ecosystem but defines an independent generic reference architecture for enterprise analytics. BPS remains the semantic authority for generic Product concepts referenced by GEARA. GEARM remains the semantic authority for generic enterprise analytics concepts referenced by GEARA.

Signature proposition: GEARM defines meaning; GEARA defines architectural responsibility for realizing that meaning.

Machine-Readable Artifact Downloads

GEARA-A1 — Canonical Architecture Registry: geara-0.1.architecture-registry.yaml
GEARA-A2 — Architecture Relationship Registry: geara-0.1.relationship-registry.yaml
GEARA-A3 — Architecture Invariant Catalogue: geara-0.1.invariant-catalogue.yaml
GEARA-A4 — RDF / OWL Representation: geara-0.1.ttl
GEARA-A5 — JSON-LD Context: geara-0.1.context.jsonld
GEARA-A6 — SHACL Constraint Layer: geara-0.1.shacl.ttl
GEARA-A7 — Structural JSON / JSON-LD Schema: geara-0.1.schema.json
GEARA-A8 — GEARA Profile Schema: geara-0.1.profile.schema.json
GEARA-A9 — Canonical Example Set: example index
GEARA-A10 — Positive Test Cases: testcase manifest
GEARA-A11 — Negative Test Cases: testcase manifest
GEARA-A12 — Automated Conformance Runner: run-geara-conformance.py
GEARA-A13 — Cross-Artifact Reconciliation Report: geara-0.1.reconciliation-report.json
GEARA-A13 — Reconciliation Runner: run-geara-reconciliation.py
GEARA-A14 — Regression Baseline: geara-0.1.regression-baseline.json
GEARA-A14 — Regression Report: geara-0.1.regression-report.json
GEARA-A14 — Regression Runner: run-geara-regression.py
GEARA-A15 — Release Closure: release README · manifest.json · SHA256SUMS · RELEASE-NOTES.md · run_release_closure.py · release-closure-report.json

GEARA-A1 establishes the canonical machine identity layer for GEARA-owned architectural elements while preserving external semantic authority boundaries. GEARA-A2 establishes canonical architecture predicates for responsibility, topology, realization, interaction, dependency/binding, Contract obligations, registry/discovery, governance/authority, provision/consequence, provenance/evidence, lifecycle/change, conformance/Profile, and adjacent-specification mapping. A2 preserves the normative rule that relationship participation does not by itself imply ontology inheritance, ownership transfer, or authority transfer. GEARA-A3 preserves all 123 section 14 architectural invariants one-to-one as stable machine records, with requirement-level, validation-class, source, and A1/A2 reference metadata; its classification metadata does not redefine the normative invariant statements. GEARA-A4 provides a conservative RDF / OWL representation of A1-A3: GEARA-owned architecture elements are represented as OWL classes, A2 relationships as RDF properties, explicit A2 symmetry is preserved, specializes is represented directionally as a subproperty of rdfs:subClassOf, and A3 invariants are first-class RDF resources. A4 does not infer domain/range, disjointness, inverse, transitive, functional, or other stronger semantics not established by the normative sources. GEARA-A5 provides the JSON-LD 1.1 compact-term context for the complete A1 architecture-element vocabulary, complete A2 relationship vocabulary, A4 representation metadata, invariant/family/type/reference namespaces, and standard RDF/OWL/SKOS/Dublin Core prefixes. It deliberately declares no default @vocab; relationship terms are IRI-valued through @type: @id; and A5 does not mint unqualified aliases for externally owned semantic or architecture concepts. GEARA-A6 provides SHACL coverage metadata for all 123 A3 invariants and conservative executable constraints for 21 Hybrid invariants where an explicit semantic or relationship collapse can be detected safely. A6 contains 24 executable NodeShapes because three invariants have both resource-level and relationship-level machine-detectable fragments. The remaining 82 Hybrid invariants are explicitly marked HybridAssessmentRequired, while all 20 Architecture-Assessment invariants are marked AssessmentOnly. Every A6 executable constraint remains partial: Architecture Assessment is still required, and SHACL PASS SHALL NOT be interpreted as complete GEARA conformance. GEARA-A7 provides the Draft 2020-12 structural JSON / JSON-LD schema for canonical architecture resources, graph documents, local terminology mappings, responsibility realizations, Contract and Registry declarations, applicability statements, Extensions, Deviations, assessment-evidence references, and conformance claims. A7 validates representation structure only and does not replace A6 SHACL or Architecture Assessment. GEARA-A8 provides the Draft 2020-12 Profile schema for governed GEARA specialization. It can select architecture elements, require capabilities, ABBs, Registry responsibilities and Contract families, declare mandatory realizations and concrete Contract schemas, add risk/provenance/assurance/resilience/technology requirements, preserve explicit Profile specialization, narrow applicability, require additional evidence, and declare Extensions or organization mappings. A8 permits only the seven A3 SHOULD invariants and thirty-two Appendix H SHOULD requirements to be tightened from SHOULD to SHALL; it does not provide a Profile-level deviation/waiver mechanism and does not permit Profile or Extension namespaces to reuse the canonical GEARA namespace. Profile-schema PASS remains structural only and SHALL NOT establish semantic preservation, applicability correctness, parent-Profile compatibility, external-specification conformance, or complete GEARA conformance. GEARA-A9 provides thirteen informative examples: one for each CP1–CP10 canonical pattern, one local terminology/architecture mapping, one governed A8 Profile, and one integrated Early Warning pattern composition. A9 examples exercise A1–A8 representation while remaining informative; example validity SHALL NOT be interpreted as normative pattern prescription or complete GEARA conformance. GEARA-A10 establishes twenty-one deterministic positive testcases under the GEARA-TC-001–GEARA-TC-021 band. TC001–TC013 are testcase copies of the A9 examples; TC014–TC021 provide direct positive coverage for Responsibility Realization, Architectural Contract Declaration, Registry Declaration, Applicability Statement, Extension, Assessment Evidence Reference, Conformance Claim, and Deviation records. testcases/manifest.json declares the expected machine-validation layers for each fixture and reserves the 100–399 bands for A11 negative classes. Positive machine PASS SHALL NOT be interpreted as completed Architecture Assessment or complete GEARA conformance. GEARA-A11 populates the reserved negative bands with twelve structural-invalid A7 fixtures (GEARA-TC-100–GEARA-TC-111), twenty-three architecture-semantic-invalid fixtures (GEARA-TC-200–GEARA-TC-222) that collectively exercise all twenty-four current A6 executable Shapes, and fourteen Profile-invalid A8 fixtures (GEARA-TC-300–GEARA-TC-313). Architecture-semantic-invalid fixtures are required to remain A7-valid and JSON-LD-expandable before their targeted A6 violation is evaluated. Expected negative detection is layer-specific evidence only and SHALL NOT be interpreted as complete architecture assessment. GEARA-A12 establishes conformance/run-geara-conformance.py as the deterministic manifest-driven machine-test runner. It executes A7 JSON Schema validation, A8 Profile-schema validation, offline A5 JSON-LD expansion, and the current A6 target-node SHACL-SPARQL constraint subset, then compares actual results with testcases/manifest.json. A12 fails closed on unsupported A6 Shape structure and does not require pyshacl. A complete suite PASS means only that all seventy testcase outcomes matched the A10/A11 oracle; it SHALL NOT be represented as complete GEARA architectural conformance because Architecture-Assessment and Hybrid judgement remain applicable. GEARA-A13 establishes reconciliation/run-geara-reconciliation.py and the deterministic reconciliation/geara-0.1.reconciliation-report.json. A13 reconciles A1/A2 identity and relationship integrity, A3 normative invariant parity, A4 RDF/OWL representation, A5 context mappings, A6 invariant/Shape coverage, A7/A8 schema reference sets, A9 examples, the A10/A11 testcase oracle, A12 runner behaviour, and specification integration. A13 PASS is internal cross-artifact evidence only; it SHALL NOT be represented as complete GEARA architectural conformance and SHALL NOT replace Architecture Assessment or Hybrid judgement.

1. Introduction

1.1 Purpose

GEARA provides a generic, technology-neutral reference architecture for governed enterprise analytics.

It defines architectural responsibilities and relationships required to move from governed Information Need and Analytical Intent through design, realization, execution, Information formation, provision, consumption, Decision integration, Outcomes, Feedback, and controlled evolution.

GEARA is intended to provide a stable architectural language across enterprises in which analytics may be distributed across domains, platforms, Products, AI capabilities, human analytical processes, agentic systems, external services, and organizational boundaries.

GEARA value proposition: GEARA provides a common architectural language so that analytical meaning, realization, governance, consumption, provenance, trust, and evolution can remain coherent even when implementations are distributed across domains, technologies, platforms, Products, AI capabilities, and organizational boundaries.

1.2 Scope

GEARA applies to enterprise analytical capabilities including, but not limited to:

direct analytical consumption;
descriptive, diagnostic, monitoring, comparative, predictive, detection, prescriptive, and exploratory analytics;
interactive analytics;
analytical chaining and synthesis;
Information Product realization and provision;
AI-assisted analytics;
Decision Support;
automated Decision integration;
human-machine analytical processes;
agentic analytical interaction;
federated enterprise analytics;
governed analytical self-service;
analytical Outcome, Value, Feedback, and evolution;
resilience, degraded operation, recovery, and lifecycle change.

GEARA is graph-shaped and SHALL NOT be interpreted as a mandatory project lifecycle, mandatory runtime sequence, or universal workflow.

1.3 Non-Goals

GEARA is not:

an analytics methodology;
a Product ontology;
a Data Product specification;
an AI Product specification;
a Decision ontology;
a cloud reference implementation;
a vendor-selection framework;
a dashboard or BI architecture;
an organizational operating model;
a mandatory centralized analytics platform;
a maturity model;
a certification authority.

GEARA does not prescribe a particular vendor, programming language, data-processing engine, AI technology, cloud, deployment topology, organizational structure, architecture notation, or analytical method.

1.4 Governing Architectural Principles

The following propositions summarize the intended character of GEARA:

GEARM defines meaning; GEARA defines architectural responsibility for realizing that meaning.
The owner builds the use case; the platform supplies the engineering.
Autonomy inside constraints; escalation at the boundary.
Analytics forms Information; Productization creates a Promise.
Implementation consolidation is permitted; semantic and architectural collapse is not.
Architecture is graph-shaped and responsibility-based, not a mandatory workflow.
Evidence should be produced by construction rather than reconstructed only after the fact.
Federation does not imply weak governance; centralization does not imply strong governance.

1.5 Normative Language

The key words SHALL, SHALL NOT, SHOULD, SHOULD NOT, and MAY indicate requirement strength.

SHALL / SHALL NOT — mandatory where the requirement is applicable to the declared conformance scope.
SHOULD / SHOULD NOT — strongly recommended architectural default. A different approach MAY be used where the alternative preserves the intent and the deviation is explainable.
MAY — permitted architectural option.

GEARA distinguishes three content classes:

Normative — establishes required GEARA meaning, architecture, or conformance;
Informative — explains normative architecture;
Illustrative — demonstrates architecture through examples and diagrams.

Examples and diagrams do not independently establish new normative requirements.

2. Purpose, Scope and Architectural Boundary

2.1 Reference Architecture Boundary

GEARA is a logical reference architecture, not a reference implementation.

Reference Architecture
    defines
architectural responsibilities and relationships

Reference Implementation
    demonstrates
one concrete realization

GEARA defines what architectural responsibilities must exist where applicable. Enterprise, domain, solution, and deployment architectures determine how those responsibilities are realized.

2.2 Architecture Derivation Chain

The canonical derivation chain is:

GEARM
Semantic Reference Model
    ↓
GEARA
Generic Reference Architecture
    ↓
GEARA Profile
Context-specific constraints
    ↓
Enterprise / Domain Architecture
Organization-specific realization
    ↓
Solution Architecture
Implementation design
    ↓
Deployment Architecture
Physical realization

A downstream architecture MAY specialize or profile GEARA, but SHALL preserve applicable GEARA and GEARM core invariants when claiming alignment.

2.3 Semantic Model and Architecture Separation

GEARM
    answers
WHAT DOES IT MEAN?

GEARA
    answers
HOW IS ARCHITECTURAL RESPONSIBILITY ARRANGED?

A GEARM Reference Model domain SHALL NOT be treated as implying a one-to-one GEARA Plane.

Core invariant: GEARM Domain != GEARA Plane.

2.4 Product Boundary

GEARA does not define Product qualification or Product Intrinsic Nature.

BPS and applicable Product-family specifications remain authoritative for generic Product semantics including Product, Product Family, Product Kind, Product Identity, Product Intrinsic Nature, Product Promise, Product Owner, Product Lifecycle, Product Port, and Product Version.

GEARA supports both:

Analytical Information
    → Direct Consumption

and:

Analytical Information
    → Information Proposition
    → intentional Productization
    → Information Product
    → Output Port
    → Consumer

Productization is optional and intentional. Analytical processing, persistence, publication, repeated consumption, an API, or marketplace registration SHALL NOT by themselves establish Product qualification.

2.5 Data Boundary

GEARA references Data and Data Product capabilities but does not absorb generic Data architecture.

GEDRM remains the semantic authority for generic enterprise data concepts. GEDRA provides the generic reference architecture for governed enterprise Data responsibilities. Applicable Data Product specifications govern Data Product semantics.

GEDRA / governed Data architecture
        ↓
Data Product / governed Data interface
        ↓
GEARA analytical dependency

2.6 AI Boundary

AIPS remains authoritative for AI Product semantics.

GEARA MAY discover, bind, invoke, monitor, and consume an AI Product as part of an Analytical Process. Such use SHALL NOT imply that the containing Analytical Process, Analytical Output, or Information Product becomes an AI Product.

AIPS
  ↓ governs
AI Product
  ↓ discovered / bound / invoked by
GEARA

2.7 Decision Boundary

GEARA models Recommendation, Decision, Action, and Outcome architecture only to the depth required to preserve analytical integration boundaries.

Information != Recommendation
Recommendation != Decision
Decision != Action
Action != Outcome

GEARA Decision integration SHALL NOT be interpreted as universal ownership of Decision semantics or Decision authority.

2.8 Technology, Topology and Organization Independence

GEARA MAY be realized through centralized, federated, distributed, domain-owned, shared-platform, SaaS, cloud, on-premises, edge, human, machine, or hybrid arrangements.

Physical consolidation or distribution does not determine conformance. Logical responsibilities and relationships do.

3. Ecosystem Relationships and Semantic Authority

3.1 Semantic Authority

Semantic Authority is the recognized authority responsible for canonical meaning, identity, distinctions, and relationships within a defined semantic scope.

Semantic Authority SHALL remain distinguishable from:

platform operation;
registry operation;
Product ownership;
analytical ownership;
approval authority;
policy authority;
implementation ownership.

3.2 Canonical Authority Boundaries

Conceptual Scope	Primary Semantic / Architectural Authority
Generic Product semantics	BPS / PIN
Generic enterprise analytics semantics	GEARM
Generic enterprise analytics architecture	GEARA
Generic enterprise Data semantics	GEDRM
Generic enterprise Data architecture	GEDRA
AI Product semantics	AIPS
Data Product semantics	Applicable Data Product specification
Context-specific architecture	Applicable GEARA Profile / enterprise or domain architecture

GEARA MAY reference externally governed concepts but SHALL NOT silently redefine them.

3.3 Imported Concepts

GEARA imports concepts such as Analytics, Analytical Process, Metric, Indicator, Prediction, Recommendation, Decision, Action, Outcome, Product, AI Product, and Data Product by reference to their appropriate semantic authorities.

Architectural use of an imported concept does not transfer semantic ownership to GEARA.

3.4 Local Terminology

A conforming architecture MAY use local terminology if the mapping preserves semantic and architectural meaning.

local label equality
    !=
semantic equivalence

A local term called Alert, Decision, Product, or Metric SHALL NOT be assumed equivalent to the corresponding GEARM or BPS concept without semantic alignment.

3.5 Profiles and Enterprise Specialization

A GEARA Profile MAY select, constrain, specialize, or tighten GEARA requirements for a defined context. A Profile SHALL NOT weaken or contradict a mandatory GEARA or GEARM core invariant while claiming alignment.

GEARA itself remains generic. HDIP or another platform architecture MAY later define an opinionated profile with mandatory registries, intent interfaces, runtime capabilities, marketplaces, or policy mechanisms.

4. Core Architectural Terminology

4.1 Plane

A Plane groups coherent primary architectural responsibilities associated with a particular aspect of governed enterprise analytics.

A Plane does not imply:

one application;
one platform;
one team;
one deployment unit;
one GEARM domain;
one mandatory step in a workflow.

4.2 Fabric

A Fabric represents a cross-cutting architectural responsibility that applies across multiple Planes. A Fabric does not represent a sequential processing stage.

4.3 Foundation

The Shared Platform / Utility Foundation represents common technical and utility capabilities upon which GEARA responsibilities may be realized. The Foundation does not establish analytical semantic authority.

4.4 Architectural Capability

An Architectural Capability is an ability the architecture must provide to support governed enterprise analytics.

A capability describes an ability such as discovering a governed Metric, resolving a Product dependency, executing an Analytical Process, exposing governed Information, or preserving provenance continuity.

4.5 Architecture Building Block

An Architecture Building Block (ABB) is a logical architectural element that groups responsibilities required to provide one or more Architectural Capabilities.

An ABB MAY be physically consolidated with other ABBs or distributed across several implementation components.

4.6 Implementation Building Block

An Implementation Building Block (IBB) is a concrete application, service, platform capability, deployed component, human process, or technology used to realize one or more ABBs.

Capability != ABB
ABB != IBB
IBB != Product ontology

4.7 Architectural Contract

An Architectural Contract is a governed declaration of expectations, references, obligations, constraints, or bindings between architectural responsibilities.

An Architectural Contract is not synonymous with an API schema, event schema, deployment descriptor, Product Promise, or implementation interface, although those may realize part of a Contract.

4.8 Registry

A Registry is a governed architectural capability for recording, indexing, discovering, and resolving identities, versions, states, and relationships of governed entities.

Registry operation SHALL NOT automatically confer semantic, Product, or analytical authority.

4.9 Governed Metadata

Governed Metadata is metadata whose meaning, identity, lifecycle, and use are managed sufficiently to support reliable analytical architecture.

4.10 Governed Operating Envelope

A Governed Operating Envelope is the set of conditions within which an authorized analytical actor or capability may operate without additional case-by-case approval.

Inside the envelope, authorized self-service MAY proceed through guardrails. Outside the envelope, applicable escalation, gate, or exception handling MAY be required.

4.11 Trust Evidence

Trust Evidence is governed evidence that assists a Consumer, policy, or assurance function in determining whether Analytical Information is appropriate for an intended use.

4.12 Analytical Assurance

Analytical Assurance is the governed evaluation of whether an Analytical Use Case, realization, or output satisfies applicable requirements for meaning, quality, security, provenance, policy, and fitness within a declared scope.

5. Architectural Principles

5.1 Semantic and Authority Principles

GEARA architectural responsibilities SHALL preserve applicable GEARM semantic invariants.
Registries, workbenches, compilers, runtimes, platforms, and APIs SHALL NOT gain semantic authority merely by storing, generating, or executing governed definitions.
Dependency and composition SHALL NOT imply ontology inheritance or ownership transfer.
Local implementation terms MAY differ where explicit mappings preserve meaning.

5.2 Responsibility and Topology Principles

GEARA Planes represent logical responsibilities, not mandatory physical platforms.
One implementation MAY realize several Planes.
One Plane MAY be distributed across several implementations.
Plane order SHALL NOT be interpreted as a mandatory lifecycle or runtime sequence.
Applicable cross-cutting obligations remain applicable regardless of the Plane path taken.

5.3 Productization and Consumption Principles

GEARA SHALL support both direct analytical consumption and intentional Information Productization.
Productization SHALL NOT be inferred from publication, repeated use, API exposure, persistence, or marketplace registration.
Productization is cross-plane and SHALL NOT be modeled as a mandatory independent analytical stage.

5.4 Governance and Self-Service Principles

Deterministic governance SHOULD be implemented as automated guardrails where reliable machine evaluation is possible.
Human gates SHOULD be reserved for genuine judgement, accountable authorization, material risk acceptance, or unresolved ambiguity.
Self-Service SHALL NOT mean ungoverned infrastructure access.
Platform-supplied engineering SHOULD reduce avoidable bespoke engineering dependency.

5.5 Evidence and Trust Principles

Runtime and material interactions SHOULD produce applicable provenance, quality, and observability evidence by construction.
Trust SHALL be evaluated contextually using evidence rather than assumed from platform, popularity, or location.
Quality and Fitness for Use SHALL remain distinguishable.
Missing or stale evidence SHALL NOT be treated as positive assurance.

5.6 Lifecycle and Evolution Principles

Semantic, Process, Logic, Product, Port, interface, realization, deployment, policy, and assurance versions SHALL remain distinguishable.
Historical interpretability SHOULD be preserved where required for provenance, audit, regulation, or Decision reconstruction.
Upstream version change SHALL NOT imply automatic downstream upgrade unless governed compatibility policy permits it.
Rollback SHALL NOT rewrite historical evidence of what actually executed.

5.7 Conformance Principles

Conformance SHALL be based on architectural responsibilities, relationships, contracts, controls, and invariants rather than naming, diagrams, or technology.
Conformance SHALL be scoped.
Machine validation complements but does not replace architecture assessment where ju:wdgement is required.
Core GEARA does not define maturity or percentage conformance scores.

6. GEARA Responsibility Model

Diagram

6.1 Architectural Decomposition

GEARA defines five primary responsibility Planes, five cross-cutting Fabrics, and a Shared Platform / Utility Foundation.

P1  Intent, Semantics & Design
P2  Discovery, Composition & Realization
P3  Analytical Runtime & Information Formation
P4  Provision, Consumption & Decision Integration
P5  Outcome, Value & Evolution

Cross-cutting Fabrics:

Governance / Policy / Assurance
Security / Privacy / Entitlement
Provenance / Evidence / Observability
Quality / Trust
Lifecycle / Version / Change

Foundation:

Shared Platform / Utility Foundation

The decomposition is logical. It SHALL NOT be interpreted as five physical systems.

6.2 P1 — Intent, Semantics & Design

The Intent, Semantics & Design Plane provides architectural responsibility for expressing what analytical capability is required and defining the governed meaning necessary to realize it.

P1 responsibilities SHOULD include, where applicable:

Information Need capture;
Analytical Intent definition;
Analytical Use Case definition;
Analytical Subject and Population definition;
governed semantic discovery and authoring;
Metric, Indicator, KPI, KRI, Threshold, Classification, Score, and State definition references;
expected Analytical Output definition;
analytical dependency declaration;
Information Proposition design;
Product Intent and Product Promise reference where Productization applies;
Consumer and access intent;
policy, quality, security, provenance, and lifecycle requirements;
versioned design metadata.

P1 answers:

What are we trying to achieve, what does it mean, and what governed analytical contract are we designing?

P1 SHALL NOT be interpreted as merely a semantic registry or metadata tool.

6.3 P1 Semantic Authority Boundary

P1 enables authoring, discovery, governance, and reference of semantics but SHALL NOT assume authority over concepts owned elsewhere.

Product semantics remain governed by BPS and applicable Product-family specifications.
Data semantics remain governed by GEDRM and applicable Data specifications.
AI Product semantics remain governed by AIPS.
GEARM remains authoritative for generic analytical semantics.

A P1 platform MAY store a Metric Definition without becoming its Semantic Authority.

6.4 P2 — Discovery, Composition & Realization

The Discovery, Composition & Realization Plane provides architectural responsibility for transforming governed analytical design into a validated, deployable analytical realization.

P2 responsibilities SHOULD include, where applicable:

Product discovery;
Semantic discovery;
Analytics Capability discovery;
AI Product discovery;
dependency resolution;
input and semantic binding;
analytical composition;
rules and Threshold binding;
AI Product invocation binding;
compatibility and impact analysis;
semantic, policy, security, quality, and runtime validation;
testing and simulation;
intent realization / compilation;
runtime binding;
deployment generation and release preparation;
registry registration;
change-impact analysis.

P2 is the primary architectural home of governed analytical self-service and the Symbiant realization model.

6.5 Intent-Driven Realization

The target interaction is:

Governed Intent
      ↓
Governed Definition
      ↓
Discover
      ↓
Compose
      ↓
Validate
      ↓
Test / Simulate
      ↓
Compile / Translate / Configure
      ↓
Bind to Runtime
      ↓
Deploy

GEARA does not require literal code compilation. An Intent Realization Capability MAY compile, interpret, configure, orchestrate, translate, or generate implementation from governed intent.

Self-service proposition: The owner builds the use case; the platform supplies the engineering.

6.6 P3 — Analytical Runtime & Information Formation

The Analytical Runtime & Information Formation Plane provides architectural responsibility for executing governed analytical realizations and forming Analytical Outputs and Information.

P3 responsibilities SHOULD include, where applicable:

governed input acquisition;
batch, streaming, event-driven, interactive, scheduled, continuous, or human-assisted execution;
Data Product consumption;
Information Product consumption;
AI Product invocation;
Metric calculation;
Indicator and Threshold evaluation;
rule execution;
classification and scoring;
detection and anomaly evaluation;
prediction and forecasting;
simulation and scenario execution;
assessment, interpretation, explanation, and synthesis;
analytical state management;
intermediate output handling;
runtime quality checks;
provenance, evidence, and operational telemetry emission.

P3 is a logical runtime responsibility and SHALL NOT require one centralized analytics platform.

6.7 Runtime Semantic Binding

P3 SHOULD execute a governed realization that explicitly binds applicable semantic and dependency versions.

Example:

Metric Definition v3
        ↓ bound by P2
Analytical Process v7
        ↓ executed by P3
Metric Observation
        ↓ provenance
used Metric Definition v3

Generated SQL, Python, rules, model invocation, or configuration SHALL NOT replace the governed semantic definition.

6.8 P4 — Provision, Consumption & Decision Integration

The Provision, Consumption & Decision Integration Plane provides architectural responsibility for exposing Analytical Information to governed Consumers and integrating that Information with downstream Recommendation, Decision, Action, and workflow environments.

P4 responsibilities SHOULD include, where applicable:

direct Analytical Output provision;
Information Product output provision;
Output Port operation;
API, stream, event, query, subscription, notification, file, interactive, or agent interface provision;
Product discovery and marketplace integration;
Consumer discovery and onboarding;
Consumer entitlement enforcement;
Consumption Event capture;
interactive analytics;
Recommendation integration;
Decision-system integration;
Action and workflow integration.

P4 SHALL NOT be interpreted as a dashboard/BI layer. Consumers MAY be humans, applications, services, agents, workflows, Products, or downstream Analytics.

6.9 Direct and Productized Provision

P4 SHALL support both:

Analytical Information
      ↓
Direct Provision
      ↓
Consumer

and:

Analytical Information
      ↓
Information Proposition
      ↓
intentional Productization
      ↓
Information Product
      ↓
Output Port
      ↓
Consumer

Direct analytical consumption is first-class and is not an inferior architectural state.

6.10 Decision Integration Boundary

P4 MAY integrate Information and Recommendations into Decision environments. It SHALL preserve the semantic distinction among:

Information
Recommendation
Decision
Action
Outcome

P4 integration SHALL NOT imply that the analytics platform universally owns Decision policy or Decision authority.

6.11 P5 — Outcome, Value & Evolution

The Outcome, Value & Evolution Plane provides architectural responsibility for observing the effect and usefulness of analytical Information and feeding governed evidence back into analytical evolution.

P5 responsibilities SHOULD include, where applicable:

Outcome Observation integration;
Consumer Feedback;
Recommendation disposition analysis;
Decision and Action Feedback;
false-positive and false-negative evidence;
analytical effectiveness assessment;
Information Product effectiveness assessment;
Value evidence and Value assessment;
usage interpretation;
Outcome attribution evidence;
drift evidence;
evolution triggers and change proposals.

P5 distinguishes operational health from analytical effectiveness.

Runtime latency / failures / throughput
    !=
Did the analytical Information help?

6.12 Governance / Policy / Assurance Fabric

The Governance Fabric spans P1–P5 and provides cross-cutting responsibility for:

policy;
control;
regulatory constraints;
semantic governance;
Product governance integration;
approvals where judgement is required;
exception and waiver handling;
assurance;
conformance;
segregation of duties.

GEARA SHOULD favor embedded guardrails over unnecessary manual gates where policy can be evaluated reliably by machine.

6.13 Security / Privacy / Entitlement Fabric

The Security Fabric spans P1–P5 and integrates enterprise security capabilities for:

identity;
authentication;
authorization;
entitlement;
purpose limitation;
privacy;
confidentiality;
data minimization;
sensitive metadata protection;
provenance access control.

Security SHALL NOT be treated as solely a P4 access concern.

6.14 Provenance / Evidence / Observability Fabric

The Provenance Fabric provides cross-cutting architectural responsibility for preserving and exposing evidence concerning design, realization, execution, consumption, Decisions, Actions, Outcomes, and evolution.

Applicable evidence MAY include:

Definition Provenance;
Semantic Provenance;
Logic and Method Provenance;
Product and AI Product Provenance;
Execution Provenance;
Consumption Provenance;
Decision and Action Provenance;
Outcome and Feedback Provenance;
operational telemetry.

Provenance and Observability MAY share implementation but SHALL remain semantically distinguishable.

6.15 Quality / Trust Fabric

The Quality / Trust Fabric provides cross-cutting responsibility for evaluating and exposing fitness and trust characteristics relevant to analytical Information.

It MAY integrate:

input quality;
semantic quality;
analytical quality;
AI/model quality;
Information Product quality;
freshness;
completeness;
uncertainty;
reproducibility;
known limitations;
Fitness for Use.

Quality dimensions SHALL NOT be collapsed into a universal opaque score by core GEARA.

6.16 Lifecycle / Version / Change Fabric

The Lifecycle Fabric provides cross-cutting responsibility for controlled evolution of semantic definitions, processes, logic, Products, dependencies, interfaces, realizations, deployments, policies, controls, and assurance evidence.

It MAY govern:

identity and version;
lifecycle state;
currentness;
compatibility;
deprecation;
retirement;
migration;
rollback;
resilience;
change impact;
historical interpretability.

6.17 Shared Platform / Utility Foundation

The Foundation MAY include:

compute;
storage;
network;
messaging and eventing;
orchestration;
scheduling;
runtime hosting;
identity infrastructure;
secrets and key infrastructure;
logging and telemetry transport;
API/service infrastructure;
deployment infrastructure.

Foundation technology enables GEARA responsibilities but SHALL NOT define GEARM semantics or inherit analytical ownership.

6.18 Productization as Cross-Plane Concern

Information Productization spans multiple Planes:

P1
Information Proposition / Product Intent / Product Promise reference

P2
Product realization / dependency resolution / lifecycle preparation

P3
Information formation

P4
Output Ports / publication / discovery / consumption

P5
Product effectiveness / feedback / evolution

Productization therefore SHALL NOT be interpreted as a separate mandatory GEARA Plane.

6.19 Self-Service / Symbiant as Architectural Property

Governed analytical self-service is an architectural operating property spanning the model, with primary realization in P1 and P2 and dependencies on P3, P4, and the cross-cutting Fabrics.

Accountable Owner
        +
Governed Platform Capability
        =
Symbiant Analytical Capability

Self-service means guarded abstraction and reusable engineering, not unrestricted tool access.

7. Architectural Interactions and Flows

7.1 Interaction Contexts

GEARA distinguishes five broad interaction contexts:

Design-Time — define governed analytical intent and meaning.
Realization-Time — transform governed design into deployable realization.
Run-Time — execute Analytics and form Information.
Consumption / Decision-Time — expose and use Information.
Evolution-Time — use evidence and Feedback to evolve analytical capability.

These contexts MAY overlap. One version MAY execute while another is being designed and validated.

7.2 Canonical Interaction Backbone

P1
Intent, Semantics & Design
        ↓ governed design
P2
Discovery, Composition & Realization
        ↓ deployable realization
P3
Analytical Runtime & Information Formation
        ↓ Information / Analytical Outputs
P4
Provision, Consumption & Decision Integration
        ↓ consumption / Decision / Action relationships
P5
Outcome, Value & Evolution
        ↓ Feedback / change motivation
        └──────────────→ P1 / P2 / P3 / P4

This backbone is explanatory, not a mandatory waterfall.

7.3 Interaction Types

GEARA interactions MAY represent:

Definition Transfer;
Reference;
Discovery;
Dependency Binding;
Validation;
Compilation / Translation;
Deployment;
Invocation;
Consumption;
Publication;
Subscription;
Event Propagation;
Evidence Emission;
Feedback;
Change Proposal;
Provenance Linkage.

Not every architectural relationship is a data flow.

7.4 Design and Discovery Iteration

P1 and P2 SHOULD support iterative design and discovery.

P1 -------------------- P2
 |                        |
 | define need            |
 |------ discover ------->|
 |<----- candidates ------|
 | select / design        |
 |------ bind intent ---->|

Design and discovery are iterative rather than strictly sequential.

7.5 Reuse Before Duplication

During design and realization, the architecture SHOULD enable discovery of existing governed semantics, Products, capabilities, and analytical assets before creation of local equivalents.

Reuse is not mandatory where existing constructs are unsuitable. The objective is governed discovery before unnecessary duplication.

7.6 Governed Analytical Design Contract

The P1→P2 interaction SHOULD provide sufficient governed design information to realize the Analytical Use Case, including where applicable:

Analytical Use Case identity;
Information Need and Analytical Intent;
semantic-definition references;
Population;
input dependency requirements;
expected output definitions;
required Analytics Capabilities;
policy, security, quality, and provenance requirements;
Consumer intent;
Product Intent where applicable.

P1 need not specify low-level implementation detail.

7.7 Realization-Time Flow

Governed Design
      ↓
Discover dependencies
      ↓
Resolve versions
      ↓
Bind semantics
      ↓
Compose Analytical Process
      ↓
Bind runtime capabilities
      ↓
Validate policy / compatibility
      ↓
Test / simulate
      ↓
Compile / translate / configure
      ↓
Prepare deployment
      ↓
Register realization

7.8 Semantic and Technical Validation

Semantic Validation
    asks
Does this mean what we claim?

Technical Validation
    asks
Can this implementation execute correctly?

Semantic validity and technical validity SHALL remain separately assessable.

7.9 Runtime Realization Contract

P2 SHOULD provide P3 with a governed runtime realization identifying, where applicable:

Analytical Process identity and version;
executable Analytical Logic;
input Product bindings;
semantic-definition bindings;
AI Product bindings;
parameterization;
rules and Threshold versions;
runtime policy;
observability and provenance configuration;
output definitions;
deployment metadata.

P3 SHOULD NOT reconstruct governed meaning independently where the design contract already defines it.

7.10 Runtime Interaction

Governed Input(s)
      +
Semantic Definitions
      +
Analytical Process
      +
Runtime Context
      ↓
Analytical Execution
      ↓
Analytical Output Occurrence(s)
      ↓
Information Formation

Runtime MAY consume Data Products and Information Products, invoke AI Products, reference semantic definitions, evaluate rules, and consume previous Analytical Outputs.

7.11 Mixed Runtime Modes

One Analytical Use Case MAY combine modes such as:

streaming detection;
batch historical baseline calculation;
request/response AI invocation;
interactive analyst exploration.

An Analytical Use Case is not required to have one runtime mode.

7.12 Evidence Side-Flow

Material execution SHOULD conceptually produce both:

Analytical / Business Output
        +
Governance / Evidence Output

Evidence MAY feed provenance, quality, observability, audit, assurance, and lifecycle capabilities.

7.13 Direct Analytical Consumption Flow

P1 Design
      ↓
P2 Realization
      ↓
P3 Analytical Output
      ↓
P4 Direct Provision
      ↓
Consumer

No Information Product is required.

7.14 Information Product Provision Flow

P1
Information Proposition
+ Product Intent
+ Product Promise reference
      ↓
P2
resolve dependencies / realize Product
      ↓
P3
form analytical Information
      ↓
P4
Information Product / Output Port / discovery / onboarding
      ↓
Consumer

7.15 Analytical Chaining

An Analytical Output MAY become a governed input to downstream Analytics without Productization at every intermediate boundary.

Process A → Output A → Process B → Output B

Productization remains optional and intentional.

7.16 AI-Assisted Analytics Flow

P1
Analytical Use Case / output semantics
      ↓
P2
Discover AI Product / validate fitness / bind version
      ↓
P3
prepare input / invoke AI Product / receive output
      ↓
classify output using GEARM semantics
      ↓
post-process / synthesize
      ↓
P4 provision

An AI Product output SHOULD be represented as its applicable semantic type such as Prediction, Classification, Score, or Explanation rather than an opaque universal AI Output type.

7.17 Interactive Analytics Flow

Consumer
   ↓
P4 interactive interface
   ↓
P3 runtime
   ↓
query / calculate / simulate
   ↓
P4 result
   ↓
Consumer

P3↔P4 interactions MAY repeat iteratively.

7.18 Exploratory-to-Governed Promotion

Exploratory analysis MAY produce candidate Metrics, rules, or insights. A candidate SHALL NOT silently become a canonical governed semantic definition.

Explore
  ↓
Candidate definition / insight
  ↓
Governed review / promotion
  ↓
P1 semantic governance
  ↓
P2 realization

7.19 Decision-Support Flow

Analytical Information
      +
Recommendation where applicable
      +
Policy / context
      ↓
Decision Actor
      ↓
Decision Event

Analytics informs the Decision; Decision authority remains separately governed.

7.20 Automated Decision Integration

Where governance permits:

Analytical Information
      +
Decision Policy
      +
Delegated Authority
      ↓
Authorized Machine Decision
      ↓
Action Integration

Technical capability SHALL NOT be treated as sufficient evidence of Decision authority.

7.21 Outcome and Feedback Flow

Consumption / Decision / Action
      ↓
Outcome
      ↓
Outcome Observation
      ↓
P5 effectiveness / Value / Feedback

Outcome evidence MAY originate from systems external to the analytical platform.

7.22 Feedback Routing

Feedback MAY target different responsibilities:

P5 → P1 semantic / intent review
P5 → P2 realization improvement
P5 → P3 runtime tuning
P5 → P4 consumer / provision improvement

Feedback SHALL NOT itself authorize semantic or Product change.

7.23 Semantic Change Propagation

Semantic Definition v3 → v4
      ↓
P2 impact analysis
      ↓
affected Processes / Products / Consumers
      ↓
new realization
      ↓
version compatibility / migration
      ↓
P5 impact monitoring

Structural compatibility SHALL NOT substitute for semantic compatibility.

7.24 Provenance Continuity

Upstream Provenance
      ↓
P2 binding evidence
      ↓
P3 execution / derivation provenance
      ↓
P4 consumption / Decision provenance
      ↓
P5 Outcome / Feedback provenance

GEARA SHOULD preserve resolvable linkage across distributed boundaries without requiring a universal centralized provenance store.

7.25 Failure and Exception Interaction

GEARA SHOULD support governed handling of failures and exceptions during discovery, validation, deployment, execution, Product invocation, Information provision, consumption, Decision integration, and Action execution.

Possible behaviors MAY include:

fail execution;
reject or quarantine;
use an approved fallback;
degrade with explicit status;
continue under policy;
escalate for human review;
require an exception.

A degraded or fallback result SHOULD NOT silently appear equivalent to the intended normal path where fitness or meaning is materially affected.

7.26 Canonical Interaction Patterns

GEARA recognizes the following interaction patterns:

F1  Governed Design Flow
F2  Intent-to-Realization Flow
F3  Analytical Runtime Flow
F4  Direct Analytical Consumption Flow
F5  Information Product Provision Flow
F6  AI-Assisted Analytics Flow
F7  Interactive Analytical Flow
F8  Analytical Chaining Flow
F9  Decision-Support Flow
F10 Automated-Decision Integration Flow
F11 Outcome & Feedback Flow
F12 Semantic Change Propagation Flow
F13 Upstream Dependency Change Flow
F14 Provenance Continuity Flow
F15 Exploratory-to-Governed Promotion Flow

These are composable patterns rather than mandatory stages.

8. Architectural Capabilities and Building Blocks

8.1 Capability Taxonomy

GEARA groups capabilities into:

Design & Semantic Capabilities;
Discovery & Realization Capabilities;
Runtime & Information Formation Capabilities;
Provision & Consumption Capabilities;
Outcome & Evolution Capabilities;
Cross-Cutting Governance Capabilities;
Shared Foundation Capabilities.

A conforming architecture SHALL realize applicable capabilities but is not required to instantiate every named ABB as a separate component.

8.2 P1 Capabilities

Applicable P1 capabilities include:

Information Need & Analytical Intent Management;
Analytical Semantic Management;
Semantic Mapping & Authority Resolution;
Analytical Design Management;
Information Proposition & Product Intent Design.

Reference ABBs

P1.1 Analytical Design Workbench
P1.2 Analytical Semantic Registry
P1.3 Semantic Mapping Registry
P1.4 Analytical Use Case Registry
P1.5 Information Proposition Designer

8.3 Analytical Design Workbench

The Analytical Design Workbench is a logical ABB supporting governed analytical design. It MAY provide:

Information Need authoring;
Analytical Intent and Use Case definition;
semantic and dependency discovery;
Population definition;
expected-output definition;
security, quality, and provenance requirement declaration;
Product Intent where applicable.

The Workbench SHALL NOT become semantic authority merely by hosting authoring functions.

8.4 Analytical Semantic Registry

The Analytical Semantic Registry provides logical capability for registration, discovery, identity, versioning, mapping, and reference of governed analytical semantic definitions.

It MAY store or reference Measures, Metrics, Indicators, KPI/KRI designations, Thresholds, Populations, Classifications, Scores, and States.

Registry != Semantic Authority

The Registry MAY be federated.

8.5 P2 Capabilities

Applicable P2 capabilities include:

Governed Discovery;
Dependency Resolution;
Compatibility & Impact Analysis;
Analytical Composition;
Policy & Constraint Validation;
Analytical Test & Simulation;
Intent Realization / Compilation;
Deployment & Release Orchestration.

Reference ABBs

P2.1 Governed Discovery Service
P2.2 Dependency Resolver
P2.3 Compatibility & Impact Analyzer
P2.4 Analytical Composition Engine
P2.5 Policy Validation Engine
P2.6 Analytical Test & Simulation Harness
P2.7 Intent Realization Engine
P2.8 Deployment & Release Orchestrator

8.6 Governed Discovery

The architecture SHOULD support discovery of governed resources including Data Products, Information Products, AI Products, semantic definitions, Analytical Processes, rules, platform capabilities, and Output Ports.

Discovery MAY federate multiple registries and authoritative stores. Discovery does not require physical metadata centralization.

8.7 Dependency Resolution

The architecture SHOULD resolve governed dependency identity, type, version, availability, authority, lifecycle state, interface, and compatibility where applicable.

Dependencies MAY include Product, semantic, AI, rule, model, temporal, reference-information, and Analytical Process dependencies.

8.8 Compatibility & Impact Analysis

Compatibility analysis SHOULD distinguish at least:

semantic compatibility;
structural compatibility;
interface compatibility;
Product compatibility;
policy compatibility;
behavioral compatibility;
Consumer compatibility;
assurance compatibility.

Impact analysis SHOULD traverse relevant dependency relationships and identify affected Processes, Products, ports, Consumers, Decisions, policies, and assurance claims where applicable.

8.9 Analytical Composition

The architecture SHOULD support governed composition of Data Products, AI Products, Information Products, semantic definitions, rules, Thresholds, analytical logic, human analytical activities, and other Analytical Processes into an Analytical Process.

Composition SHALL NOT imply ontology inheritance or ownership transfer.

8.10 Intent Realization Engine

The Intent Realization Engine is the logical ABB for translating governed analytical design into deployable realization.

It MAY perform:

semantic binding;
dependency binding;
runtime selection;
logic or configuration generation;
policy injection;
provenance and observability instrumentation;
deployment descriptor generation;
registry registration.

The engine MAY compile, interpret, configure, translate, orchestrate, or generate. It SHALL NOT become semantic authority merely because it generates an implementation.

8.11 P3 Capabilities

Applicable P3 capabilities include:

Governed Input Acquisition;
Analytical Execution;
AI Product Invocation;
Rule / Condition / Threshold Evaluation;
Analytical State Management;
Information Formation & Synthesis;
Runtime Evidence Emission.

Reference ABBs

P3.1 Governed Input Adapter
P3.2 Analytical Runtime
P3.3 AI Product Gateway
P3.4 Rule & Condition Evaluation Engine
P3.5 Analytical State Manager
P3.6 Information Formation / Synthesis Engine
P3.7 Runtime Evidence Emitter

8.12 Analytical Runtime

The Analytical Runtime represents logical responsibility for executing governed Analytical Processes.

It MAY be realized using SQL, stream processing, rules, statistics, simulation, ML/AI, human analytical tasks, agents, or combinations.

Analytical Runtime ABB != single analytics platform

8.13 AI Product Gateway

Where useful, an AI Product Gateway MAY provide governed AI Product discovery, version binding, input preparation, invocation, response capture, provenance reference, failure handling, and fallback handling.

A separate gateway component is not mandatory if equivalent capability exists elsewhere.

8.14 Analytical State Management

Where an Analytical Use Case maintains state, the architecture MAY provide governed state persistence and transition management with identity, effective time, semantic definition, and transition evidence.

Examples include Early Warning State, Fraud Monitoring State, or Portfolio Risk State.

8.15 Runtime Evidence Emission

Runtime SHOULD emit material evidence such as execution identity, Process version, semantic versions, input Product versions, AI Product version, logic version, output identifiers, quality observations, fallback mode, and operational telemetry where applicable.

8.16 P4 Capabilities

Applicable P4 capabilities include:

Governed Information Provision;
Information Product Provision;
Consumer Discovery & Access;
Consumer Entitlement;
Interactive Analytics;
Consumption Event Capture;
Recommendation Integration;
Decision Integration;
Action / Workflow Integration.

Reference ABBs

P4.1 Information Provision Gateway
P4.2 Information Product Publisher
P4.3 Product / Marketplace Adapter
P4.4 Consumer Access Gateway
P4.5 Interactive Analytics Interface
P4.6 Consumption Evidence Emitter
P4.7 Recommendation Adapter
P4.8 Decision Integration Adapter
P4.9 Action / Workflow Adapter

8.17 Information Provision Gateway

The Information Provision Gateway represents logical responsibility for exposing governed Analytical Information through interfaces such as APIs, queries, events, streams, notifications, subscriptions, files, interactive experiences, or agent interfaces.

It MAY provide version routing, access enforcement, semantic metadata exposure, quality evidence, and provenance references.

It is not necessarily one API gateway.

8.18 Information Product Publisher

Where Productization applies, the architecture MAY support Product registration, Output Port registration, versioning, Product Promise publication, Consumer onboarding, subscription, and Product observability.

Information Provision and Information Product Provision SHALL remain distinguishable.

8.19 P5 Capabilities

Applicable P5 capabilities include:

Outcome Observation Integration;
Feedback Capture;
Analytical Effectiveness Evaluation;
Value Assessment;
false-positive / false-negative evaluation;
Consumer Experience Assessment;
Evolution Initiation.

Reference ABBs

P5.1 Outcome Integration Service
P5.2 Feedback Service
P5.3 Analytical Effectiveness Evaluator
P5.4 Value Evidence & Assessment Capability
P5.5 Analytical Learning / Evolution Manager

Learning in this ABB name means governed analytical/organizational feedback and does not imply machine learning.