1. Provenance-aware faceted search Peter Fox Stephan Zednik Patrick West Tetherless World Constellation, RPI EGU 2010

2. Provenance def: A record of ownership of a work of art or an antique, used as a guide to authenticity or quality Documentation of processes in a digital object’s life cycle Origin or source from which something comes, manner of manufacture, production, or discovery, documented in sufficient detail to allow reproducibility or validation 2

3. Provenance-related Use Cases What calibrations have been applied to this image? 1 What were the cloud cover and seeing conditions during the observation period of this image? 1 Why does this image look bad? 1 What are the processing and parameter differences between the MODIS Daily AOT Data Product vs. the MODIS Monthly AOT Data Product? 2 3 1 SPCDIS http://tw.rpi.edu/portal/SPCDIShttp://tw.rpi.edu/portal/SPCDIS 2 MDSA http://tw.rpi.edu/portal/MDSAhttp://tw.rpi.edu/portal/MDSA

4. One* View of Provenance in e- Science Assume the provenance of objects is represented by an annotated causality graph For our purpose a provenance graph is a representation of a record of past execution Process Artifact 4 * One of MANY. This representation of provenance satisfies our Need to capture processing history and information dependency Artifact Process

5. Modeling a Provenance Use Case What calibrations have been applied to this image? Provenance concepts Solar Science concepts Data Product Data Processing concepts Data Filtering Process Data Filtering Process Raw Image Optics Calibration Process Data Calibration Process Flat-field Calibration Angle of Incidence Calibration Junk Data Filter 5

6. Old way Tetherless World Constellation 6

7. 7

8. 8 Resulting Image, no further information provided

9. Provenance aware faceted search Tetherless World Constellation 9

10. Knowledge Base with Provenance and Domain Models in Alignment 10 Data Capture Data Capture Instrument Justification Conclusion Source Rule CSR Image #MyImage #MyImage _justificatio n #He-1083 nm Continuum Image Capture 2009-12- 16T17:30:00- 08:00 #CHIP NodeSet SourceUsage xsd:DateTim e hasSourceUsage rdf:type rdf:datatype rdf:type hasInferenceRule

11. Foundation Ontologies/Vocabularies Ontologies designed with reuse and extension in mind – Some contain loosely-scoped high-level concepts – Some narrow focus with intent to be used in diverse domains Simplify information exchange and interoperability Examples – FOAF – SKOS – Dublin Core – OWL Time 11 OWL Time

12. Foundation Data Provenance Models Provenance Markup Language (PML) – OWL ontology – from automated intelligent systems community – Provenance as information justification – Additional Semantic support for explanation and trust Open Provenance Model (OPM) – Language-agnostic* model – from workflow community – Provenance as artifact creation – Additional support for modeling controlling processes and actors 12

13. Interoperability with Provenance Tools 13 Probe-It! http://trust.utep.edu/probeit/ Inference Web Browser http://inference-web.org/iwbrowser

14. Proof Markup Language (PML) Justification – Explanation – Causality graph Provenance – Conclusion – Source – Engine – Rule Trust – Trust/Belief metrics NodeSet Justification Conclusion NodeSet Justification Conclusion NodeSet Justification Conclusion Engine Rule hasAntecedentList hasSourceUsage hasInferenceRule hasInferenceEngine SourceUsage Source DateTime 14

15. Open Provenance Model Agents – Catalyst and controlling entity of a process Processes – Action or Series of actions performed resulting in new artifacts Artifacts – Immutable piece of state Roles – Non-semantic flat tags used to provide context in relations Artifact Process wasGeneratedBy(Role) Agent Artifact used(Role) wasControlledBy(Role) Artifact wasDerivedFrom(Role) Process wasGeneratedBy(Role) wasTriggeredBy(Role) 15

16. SourceUsage Concept Alignment (PML) Data Capture Data Capture Instrument Data Product Data Calibration Data Calibration Raw Data NodeSet Justification Conclusion NodeSet Justification Conclusion hasAntecedentList Observation Period hasSourceUsage Source DateTime Rule Engine Rule Calibration 16

17. Concept Alignment (OPM) 17 Data Capture Data Capture Instrument Data Product Data Calibration Data Calibration Raw Data Observation Period Calibration Artifact Process wasGeneratedBy(DataCalibrationProcess) Agent Artifact wasControlledBy(Instrument) Process used(RawData) wasGeneratedBy(DataCaptureProcess) Artifact used(DataCalibration) Artifact used(Timestamp)

18. Alignment via Ontology Constructs Use ontology constructs to map a relationship between concepts in different domains Can be defined in a separate ontology than the models being mapped Does not require a change to the source models! OWL – owl:equivalentClass – owl:equivalentProperty – owl:sameAs RDFS – rdfs:subClassOf – Rdfs:subPropertyOf 18 Instrument Source rdfs:subClassOf Calibration Rule rdfs:subClassOf Conclusion Data Product rdfs:subClassOf

19. Direct Alignment using Rules* Rules provide conditional logic on semantic constructs outside application logic Rules can be updated or tweaked without requiring an application update. Easily shared and managed Provides for more complex mapping than ontology constructs 19 *Many rule systems exist, this slide uses the Semantic Web Rule Language (SWRL) ex:Instrument(?x)  pmlp:Sensor(?x) pmlp:Information(?x) ^ pmlp:hasURL(?x,?url) ^ swrlb:endsWith(?url, ”.hsh.fts ”)  Ex:CHIPIntensityImage(?x)

20. Querying/Interrogat ing the Knowledge Base Back to the use case: What calibrations have been applied to this image? We construct a query returns any individuals with type Calibration used as the InferenceRule in the justification from any artifact the current artifact was derived from. We assume that any calibration applied to an artifact the current artifact was derived from can also be considered as ‘applied’ to the current artifact, and that the wasDerivedFrom property is transitive 20 #Image #_A2 #Intermediate2 #_A1 #Intermediate1 #_A0 #RawImage #_A0 wasDerivedFrom #Angle of Incidence Calibration #Angle of Incidence Calibration #Flat Field Calibration #Flat Field Calibration rdf:type hasInferenceRule

21. Final Remarks/Discussion Provenance concepts describe how domain concepts are related Domain and provenance models should be independent, but aligned Aligning with a well-supported provenance model can enhance interoperability and tool support Aligned knowledge base supports complex multi-domain query and search 21

22. Links PML: http://inference-web.org/2007/primer/http://inference-web.org/2007/primer/ OPM: http://openprovenance.org/http://openprovenance.org/ SWRL: http://www.w3.org/Submission/SWRL/http://www.w3.org/Submission/SWRL/ Inference Web http://inference-web.orghttp://inference-web.org Probe-It! http://trust.utep.edu/probe-it/http://trust.utep.edu/probe-it/ SPCDIS http://tw.rpi.edu/portal/SPCDIShttp://tw.rpi.edu/portal/SPCDIS MDSA http://tw.rpi.edu/portal/MDSAhttp://tw.rpi.edu/portal/MDSA http://tw.rpi.edu/portal/SPCDIS http://tw.rpi.edu/portal/ Contacts: – pfox@cs.rpi.edu pfox@cs.rpi.edu – zednis@rpi.edu zednis@rpi.edu – westp@rpi.edu westp@rpi.edu 22

23. Acknowledgements NASA/GSFC – Gregory Lepkotuh – Chris Lynnes UTEP/CyberSHARE – Paulo Pinheiro da Silva – Nicholas del Rio RPI – Patrick West 23