W3C

Provenance Dimensions

This is a list of important dimensions in provenance that the group identified in order to guide the collection of use cases. Content

Object - what the provenance is about
Attribution - provenance as the sources or entities that were used to create a new result
- Responsibility - knowing who endorses a particular piece of information or result
- Origin - recorded vs reconstructed, verified vs non-verified (eg with digital signatures), asserted vs inferred

Process - provenance as the process that yielded an artifact ## Reproducibility (eg workflows, mashups, text extraction)
Data Access (e.g. access time, accessed server, party responsible for accessed server)

Evolution and versioning ## Republishing (e.g. retweeting, reblogging, republishing)
Updates (eg a document that assembles content from various sources and that changes over time)

Justification for Decisions - capturing why and how a particular decision is made ## argumentation - what was considered and debated (eg pros and cons) before reaching a solution
hypothesis management (eg in HLCS scientific discourse task when complementary/contrary evidence is provided by different sources)
why-not questions - capturing why a particular choice was not made
Entailment - given the results to a particular query in a reasoning system or DB, capture how the system produced an answer given what axioms or tuples it contained that led to those results

Management

Publication - Making provenance information available on the web (how do you expose it, how do you distribute it)
Access - Finding and querying provenance information
- Finding the provenance information, perhaps through an authoritative service
- Query formulation and execution mechanisms

Dissemination control - Using provenance to track the policies for when/how an entity can be used as specified by the creator of that entity ## Access Control - incorporate access control policies to access provenance information
Licensing - stating what rights the object creators and users have based on provenance
Law enforcement (eg enforcing privacy policies on the use of personal information)
Scale - how to operate with large amounts of provenance information

Use

Understanding - End user consumption of provenance.
- abstraction, multiple levels of description, summary
- presentation, visualization

Interoperability - combining provenance produced by multiple different systems
Comparison - finding what's in common in the provenance of two or more entities (eg two experimental results)
Accountability - the ability to check the provenance of an object with respect to some expectation
- Verification - of a set of requirements
- Compliance - with a set of policies

Trust - making trust judgements based on provenance
- Information quality - choosing among competing evidence from diverse sources (eg linked data use cases)
- Incorporating reputation and reliability ratings with attribution information
Imperfections - reasoning about provenance information that is not complete or correct **Incomplete provenance
- Uncertain/probabilistic provenance
- Erroneous provenance
- Fraudulent provenance

8Debugging