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Difference between revisions of "FHIR Provenance Resource Use Cases"
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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. | This is a list of important dimensions in provenance that the group identified in order to guide the collection of use cases. | ||
Content | 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 | 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 | 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 | |
Revision as of 10:23, 18 November 2015
Back to HL7 FHIR Security Topics HL7 FHIR Provenance Resource
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
