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Difference between revisions of "FHIR Provenance Resource Use Cases"

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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]
  
==W3C==
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==[[W3C PROV Use Cases and Requirements]]==
=[http://wiki.hl7.org/index.php?title=HL7_FHIR_Provenance_Resource Provenance Dimensions]=
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==[[ONC S&I DPROV Use Cases and Requirements]]==
 
 
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
 

Latest revision as of 05:07, 2 December 2015