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Usage guide

This guide is organized as a practical user journey so you can move from foundational checks to advanced rule logic in a predictable order.

Start with single-instance and graph-level validation to confirm structural correctness, then expand into uniqueness and time-series quality, and finally use conditional and chained conditional logic for derived operational outcomes and multi-step reasoning.

You can follow the journey end-to-end when onboarding, or jump directly to the section that matches your current use case.

For how rules are stored and published, see Rule sources.

This usage guide follows one validation journey:

  1. Single instance data quality validation - Expected outcome: per-instance pass/fail findings for required fields and basic constraints.
  2. Graph consistency - Expected outcome: detected relationship and model-consistency issues across connected instances (SPARQL / cross-node).
  3. Edge existence - Expected outcome: typed-edge cardinality pass/fail for MultiEdgeConnection relations (dqs:edgeExistenceConstraint).
  4. Uniqueness - Expected outcome: duplicate-value detection with actionable violation records for cleanup and rerun.
  5. Time series - Expected outcome: signal-quality findings for gaps, stale data, range violations, and temporal anomalies.
  6. Conditional logic - Expected outcome: derived rule outputs (RuleEngineResult) for operational classification and automation.
  7. Chained conditional logic - Expected outcome: multi-stage inferred outcomes with lineage (causedBy) across dependent rules.

Start with context

Validation journey

  1. Single instance data quality validation — confirm that rules work (run_validation() vs estate historic), then sync-cursor and historic orchestration
  2. Graph consistency
  3. Edge existence
  4. Uniqueness
  5. Time series
  6. Conditional logic
  7. Chained conditional logic

Supporting operations