Provenance Management in Scientific Workflows
Summary
Provenance management in scientific workflows encompasses the systematic capture, organisation and interrogation of information detailing the origin, transformations and dependencies of data and computational processes. As research becomes increasingly data-intensive and collaborative, clear records of how datasets are generated, processed and analysed are essential for ensuring validity, reproducibility and transparency. A robust provenance framework integrates with workflow systems to record provenance graphs, metadata and version histories in real time, enabling scientists to trace analytic steps, diagnose errors, audit outcomes and share reproducible pipelines. Techniques range from declarative templating and manifest specifications to automated capture via instrumentation and interoperable standards. By linking code, parameters, intermediate files and computational environments, provenance management fosters trust in results, streamlines validation across distributed teams and underpins FAIR (findable, accessible, interoperable, reusable) research principles. Emerging approaches emphasise low-overhead integration, support for heterogeneous tools and dynamic workflows, and advanced visualisation of lineage information to guide decision-making and regulatory compliance.
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Provenance Management in Scientific Workflows publication trend
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Technical terms
Provenance management: The practise of recording, organising and querying the history of data and processes in a workflow.
Scientific workflow: A structured sequence of computational or experimental steps designed to achieve a specific research outcome.
Data lineage: The trace of data origins, transformations and movement through a workflow.
Provenance graph: A directed acyclic graph representing entities (data, code), activities (processes) and their relationships.
Data Manifest specification: A declarative format that lists data inputs and versions for reproducible workflow execution.
Augmented lineage: An extended lineage concept that incorporates interpretability metadata for complex analyses involving AI/ML and UDFs.
References
- A Templating System to Generate Provenance. IEEE Transactions on Software Engineering (2018).
- Augmented lineage: traceability of data analysis including complex UDF processing. The VLDB Journal (2022).
- Modeling the Data Provenance of Relational Databases Supporting Full-Featured SQL and Procedural Languages. Applied Sciences (2022).
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