ProvenanceSystemen

ProvenanceSystemen (in German, Provenance-Systeme) are information systems designed to capture, store, and query metadata about the origins and transformations of data and artifacts. They support data provenance, the recorded history of where data comes from, how it was produced, and by whom. The primary goal is to improve traceability, accountability, reproducibility, and trust in data-driven processes.

Key concepts include data lineage (the lineage of data products across steps), process provenance (the activities and workflows that produced data), actors and agents, events and timestamps, and custody or ownership changes. Provenance information is typically stored as graphs or structured metadata and may be integrated with data catalogs, workflow engines, and version control.

Architectures vary. Some systems use centralized stores or metadata catalogs; others rely on distributed ledger technology or graph databases to ensure immutability and efficient querying. Provenance data can be generated automatically by workflow systems, data pipelines, or manual annotations. Interoperability is supported by standards such as the W3C PROV family (PROV-DM, PROV-O), and by historical models like the Open Provenance Model (OPM).

Applications span scientific research, data governance, manufacturing and supply chains, healthcare, finance, and digital media. In practice, provenance systems support audit trails, reproducible experiments, compliance reporting, and quality assurance. Challenges include ensuring privacy and data minimization, scaling to large or streaming datasets, maintaining data quality, and achieving interoperability across diverse tools and domains.