The provenance of data products generated by complex transformations such as workflows is of considerable value to scientists. From it, one can ascertain the quality of the data based on its ancestral data and derivations, track back sources of errors, allow automated re-enactment of derivations to update a data, and provide attribution of data sources. Provenance is also essential to the business domain where it can be used to drill down to the source of data in a data warehouse, track the creation of intellectual property, and provide an audit trail for regulatory purposes.
In this paper we create a taxonomy of data provenance techniques, and apply the classification to current research efforts in the field. The main aspect of our taxonomy categorizes provenance systems based on why they record provenance, what they describe, how they represent and store provenance, and ways to disseminate it. Our synthesis can help those building scientific and business metadata-management systems to understand existing provenance system designs. The survey culminates with an identification of open research problems in the field.
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