provenanceaware

Provenanceaware, or provenance-aware, describes systems, datasets, or workflows that are designed to capture, preserve, and expose provenance information. Provenance refers to the history of a data item or artifact: where it came from, the processes or steps it underwent, and the people or organizations involved in its creation and modification. In provenance-aware designs, this information is a first-class asset, collected automatically where possible and made accessible for querying, reasoning, and verification.

Core concepts include entities, activities, and agents; relationships such as used, wasGeneratedBy, wasAssociatedWith; and provenance graphs that trace lineage across data and processes. To promote interoperability, provenance-aware systems often adopt standard models such as the W3C PROV family (PROV-DM, PROV-O) or the earlier Open Provenance Model.

Applications span scientific data workflows, data governance and regulatory compliance, audit trails for quality assurance, and supply chain transparency. In machine learning and data science, provenance enables tracking of data sources, preprocessing steps, model training, and evaluation, supporting reproducibility and accountability.

Challenges include potential performance and storage overhead, privacy and security concerns, incomplete or noisy provenance data, and the need for domain-appropriate extensions. Adoption depends on clear incentives, governance policies, and alignment with interoperable standards to ensure usable, shareable provenance across systems.