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| | [http://wiki.hl7.org/index.php?title=HL7_FHIR_security_topics Back to HL7 FHIR Security Topics] | | [http://wiki.hl7.org/index.php?title=HL7_FHIR_security_topics Back to HL7 FHIR Security Topics] |
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| | [http://wiki.hl7.org/index.php?title=HL7_FHIR_Provenance_Resource HL7 FHIR Provenance Resource] | | [http://wiki.hl7.org/index.php?title=HL7_FHIR_Provenance_Resource HL7 FHIR Provenance Resource] |
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| − | ==W3C== | + | ==[[W3C PROV Use Cases and Requirements]]== |
| − | =[http://wiki.hl7.org/index.php?title=HL7_FHIR_Provenance_Resource Provenance Dimensions]= | + | ==[[ONC S&I DPROV Use Cases and Requirements]]== |
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| − | This is a list of important dimensions in provenance that the group identified in order to guide the collection of use cases.
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| − | Content
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| − | ## Object - what the provenance is about
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| − | ## 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
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| − | ## Origin - recorded vs reconstructed, verified vs non-verified (eg with digital signatures), asserted vs inferred
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| − | ## Process - provenance as the process that yielded an artifact ## Reproducibility (eg workflows, mashups, text extraction)
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| − | ## Data Access (e.g. access time, accessed server, party responsible for accessed server)
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| − | ## Evolution and versioning ## Republishing (e.g. retweeting, reblogging, republishing)
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| − | ## Updates (eg a document that assembles content from various sources and that changes over time)
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| − | ## 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
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| − | ## hypothesis management (eg in HLCS scientific discourse task when complementary/contrary evidence is provided by different sources)
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| − | ## why-not questions - capturing why a particular choice was not made
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| − | ## 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
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| − | Management
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| − | ## Publication - Making provenance information available on the web (how do you expose it, how do you distribute it)
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| − | ## Access - Finding and querying provenance information ## Finding the provenance information, perhaps through an authoritative service
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| − | ## Query formulation and execution mechanisms
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| − | ## 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
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| − | ## Licensing - stating what rights the object creators and users have based on provenance
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| − | ## Law enforcement (eg enforcing privacy policies on the use of personal information)
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| − | ## Scale - how to operate with large amounts of provenance information
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| − | Use
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| − | ## Understanding - End user consumption of provenance. ## abstraction, multiple levels of description, summary
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| − | ## presentation, visualization
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| − | ## Interoperability - combining provenance produced by multiple different systems
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| − | ## Comparison - finding what's in common in the provenance of two or more entities (eg two experimental results)
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| − | ## Accountability - the ability to check the provenance of an object with respect to some expectation ## Verification - of a set of requirements
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| − | ## Compliance - with a set of policies
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| − | ## Trust - making trust judgements based on provenance ## Information quality - choosing among competing evidence from diverse sources (eg linked data use cases)
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| − | ## Incorporating reputation and reliability ratings with attribution information
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| − | ## Imperfections - reasoning about provenance information that is not complete or correct ## Incomplete provenance
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| − | ## Uncertain/probabilistic provenance
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| − | ## Erroneous provenance
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| − | ## Fraudulent provenance
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| − | ## Debugging
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