Provenance Graph

Records every state transition as an append-only chain of semantically rich events. Each node represents a data artifact or physical product at a specific point in time, while edges capture the transformation process, responsible agent, and timestamp. This creates a cryptographically verifiable audit trail that cannot be retroactively altered, essential for FDA 21 CFR Part 11 compliance in pharmaceutical manufacturing.

Implements the PROV-O ontology to represent provenance information in an interoperable, machine-readable format. The model uses three core entity types:

• Entity: The physical or digital artifact being tracked
• Activity: The process that generated or modified an entity
• Agent: The person, software, or machine responsible for an activity This standardization enables cross-system provenance queries across MES, LIMS, and ERP platforms.

Unlike simple lineage logs, a provenance graph explicitly models wasDerivedFrom and wasInformedBy relationships between entities and activities. This enables engineers to perform upstream impact analysis—identifying all downstream batches affected by a contaminated raw material lot—and downstream root cause analysis—tracing a finished good defect back through every intermediate transformation to the originating process parameter deviation.

Maintains a time-versioned history of every entity, allowing queries like 'Show me the state of Batch 7342 on 2024-03-15 at 14:30 UTC.' Supports branching provenance for what-if scenarios, such as simulating alternative process parameters without corrupting the primary production record. This temporal reasoning capability is critical for investigating deviations that occurred during a specific shift or maintenance window.

Every transformation edge is annotated with agent attribution metadata that captures not just who performed an action, but the role, authorization level, and digital signature of the responsible party. In automated environments, this extends to software agents and PLC programs, creating a complete chain of custody that distinguishes between manual operator interventions and autonomous system decisions for regulatory inspection.

Ingests provenance fragments from disparate systems—SCADA historians, LIMS sample records, ERP material receipts—and performs entity resolution to link records referring to the same physical asset. A single 'Lot 8891' node in the graph may consolidate provenance information from five different source systems, providing a unified, contradiction-free lineage view that eliminates the data silos common in brownfield manufacturing facilities.