Evolution of Workflow Provenance    Information in the Presence of        Custom Inference Rules     C. Strubulis, Y. Tzit...
SWPM 2012                                          2Outline • Provenance-based inference rules • Knowledge evolution of pr...
SWPM 2012                                                  3What is Provenance? • Etymology   ▫ French verb “provenir”   ▫...
SWPM 2012                                                                 4Why is Provenance Important?  anon4877_base_200...
SWPM 2012                                                 5Motivation • Motivation   ▫ Reduce the amount of provenance inf...
SWPM 2012                                     6  Storage Space Challenge          Extra data for                          ...
SWPM 2012                                                    7User Input Challenge                       Record           ...
SWPM 2012                                   8User Input Challenge   Day 1              Day 11111            …………….        ...
SWPM 2012                                                  9   Error Correction ChallengeScientific workflow   Provenance ...
SWPM 2012                                                                10   Approach-Results• Our Approach  ▫ Dynamic co...
SWPM 2012                                                                             11 The Assumed Provenance Model   Mo...
SWPM 2012                                               12    The three inference rulesR1:If an actor has carried out one ...
SWPM 2012                   133d Reconstruction process
SWPM 2012                                                                    14Actors - Activities• If an actor has carrie...
SWPM 2012                                                                15Devices - Activities If an object was used for ...
SWPM 2012                                                               16Information Objects - Events If a physical objec...
SWPM 2012                                          17Outline • Provenance-based inference rules • Knowledge evolution of p...
SWPM 2012                                               18Knowledge Evolution                                           In...
SWPM 2012                                                                            19    Example• Consider a KB containi...
SWPM 2012                                                20Foundational vs Coherence • Foundational Viewpoint   ▫ Each pie...
SWPM 2012                                                        21Deletion of a fact • Foundational:   ▫ All implicit dat...
SWPM 2012                                                                          22    Example• Consider a KB containing...
23• Update request:   Starc was not responsible for Capture 1                 Foundational                                ...
SWPM 2012                                         24Complexity analysis   • Similar operations can be also defined for    ...
SWPM 2012                                                                         25Conclusion • Provenance-based Inferenc...
26Thank you for your attention.
27
SWPM 2012                                                           28On repository policies                        Needs ...
SWPM 2012            29General statistics
SWPM 2012          30Space evaluation
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Evolution of Workflow Provenance Information in the Presence of Custom Inference Rules

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The presentation was delivered by FORTH at the 3rd International Workshop on the role of Semantic Web in Provenance Management 2012 (SWPM2012) in Heraklion, Greece on 28th of May 2012.

Abstract:
Workflow systems can produce very large amounts of provenance information. In this paper we introduce provenance-based inference rules as a means to reduce the amount of provenance information that has to be stored, and to ease quality control (e.g., corrections). We motivate this kind of (provenance) inference and identify a number of basic inference rules over a conceptual model appropriate for representing provenance. The proposed inference rules concern the interplay between (i) actors and carried out activities, (ii) activities and devices that were used for such activities, and, (iii) the presence of information objects and physical things at events. However, since a knowledge base is not static but it changes over time for various reasons, we also study how we can satisfy change requests while supporting and respecting the aforementioned inference rules. Towards this end, we elaborate on the specification of the required change operations.

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Evolution of Workflow Provenance Information in the Presence of Custom Inference Rules

  1. 1. Evolution of Workflow Provenance Information in the Presence of Custom Inference Rules C. Strubulis, Y. Tzitzikas, M. Doerr and G. Flouris {strubul, tzitzik, martin, fgeo}@ics.forth.grInstitute of Computer ScienceFoundation for Research SWPM Workshopand Technology – Hellas 28/05/2012ICS-FORTH Crete, HerakleionComputer Science DepartmentUniversity of Crete
  2. 2. SWPM 2012 2Outline • Provenance-based inference rules • Knowledge evolution of provenance information
  3. 3. SWPM 2012 3What is Provenance? • Etymology ▫ French verb “provenir” ▫ Meaning: to come forth, originate • The Merriam-Webster Online Dictionary ▫ the origin, source ▫ the history of ownership of a valued object or work of art or literature.
  4. 4. SWPM 2012 4Why is Provenance Important? anon4877_base_20060331.jpg anon4877_lesion_20060401.jpg Reproducibility Data quality Attribution Informational How were these images created? Was any pre-processing applied more!)raw data? Provenance is as (or to the important as the experimental Who created them? What’s the difference? results Are they really from the same patient?
  5. 5. SWPM 2012 5Motivation • Motivation ▫ Reduce the amount of provenance information that has to be stored (produced by scientific workflow systems) ▫ Reduce the time and human effort in the case of manual ingestion of provenance metadata in repositories ▫ Elimination of errors starting from the input data  Reduction of error search space -> Easier error correction
  6. 6. SWPM 2012 6 Storage Space Challenge Extra data for Adoption of Inference provenanceStorage mechanismsSpace Data Production Time
  7. 7. SWPM 2012 7User Input Challenge Record Provenance Digital Metadata Ingestion RDF/S Repository
  8. 8. SWPM 2012 8User Input Challenge Day 1 Day 11111 ……………. Adoption of Inference mechanisms Time
  9. 9. SWPM 2012 9 Error Correction ChallengeScientific workflow Provenance Data Metadata record ingestion Which one to correct? From where to Adoption of Inference start mechanisms searching?
  10. 10. SWPM 2012 10 Approach-Results• Our Approach ▫ Dynamic completion of the stored knowledge by logical assumptions – inferences ▫ Identify and specify some basic provenance- Query results based inference rules + Inferences ▫ In addition, we tackle the knowledge evolution requirements  The question is how we can satisfy update requests while still supporting the aforementioned provenance-based inference rules.  Operations: Disassociation, Contraction
  11. 11. SWPM 2012 11 The Assumed Provenance Model Most provenance models have P12 was E73 Information E5 Event similar concepts!!! present at Object IsA P9 forms P128 part of P12 was carries E7 Activity present at P14 E24Small part of P16 was used carried PhysicalCIDOC CRM for out by Man-Made Thing E22 Man-made IsA E39 Actor Object (Device) P46 forms part of
  12. 12. SWPM 2012 12 The three inference rulesR1:If an actor has carried out one activity,then (s)he has carried out all of its subactivities.R2:If an object (device) was used for an event,then all parts of that object were also used forthat event.R3:If a physical object that carries an information objectwas present at an event, then that information objectwas also present at the event.
  13. 13. SWPM 2012 133d Reconstruction process
  14. 14. SWPM 2012 14Actors - Activities• If an actor has carried out one activity, then (s)he has carried out all of its subactivities. Starc P14 carried out Laser scanning P14 carried out Institute by acquisition John by P9 forms part of Detailed sequence of shots P14 carried P14 carried out by out by P9 forms part of Capture 1 ……………….. Capture 10 P14 carried out by
  15. 15. SWPM 2012 15Devices - Activities If an object was used for an event, then all parts of the object were used for that event too. P16 was used for Detailed P16 was sequence of Multiviewdome device used for shots P46 forms P46 forms part of part of Nikon D90 AF-5_NIKKOR 18-105mm ........ Nikon D300 P16 was used for
  16. 16. SWPM 2012 16Information Objects - Events If a physical object that carries an information object was present at an event, then that information object was present at the event too. P12 was present at 3D reconstruction Part of column of Ramesses II Detailed sequence of shots P128 ……… carries Capture 1_10 P12 was present Information in hieroglyphics at
  17. 17. SWPM 2012 17Outline • Provenance-based inference rules • Knowledge evolution of provenance information
  18. 18. SWPM 2012 18Knowledge Evolution Inference rules Evolution • Updating our knowledge is essential!! • Requests for adding/removing information • The use of inference rules introduces difficulties with respect to the evolution of knowledge
  19. 19. SWPM 2012 19 Example• Consider a KB containing the Starc P14 carried Laser scanning activities of Laser Scan Institute out by acquisition Acquisition P9 forms part of• Starc is propagated to all the subactivities of Laser Scan Detailed sequence Acquisition by rule R1 of shots• Update request: P14 Starc was not responsible for carried P9 writing the Capture 1_1 out by forms part of• There are two cases to handle the Capture 1 Capture 10 request
  20. 20. SWPM 2012 20Foundational vs Coherence • Foundational Viewpoint ▫ Each piece of our knowledge serves as a justification for other beliefs ▫ Implicit facts are supported by the explicit ones ▫ Explicit knowledge is more important than implicit one • Coherence Viewpoint ▫ Every piece of knowledge is self-justified ▫ Implicit needs no support from explicit ▫ Explicit and implicit have the same value
  21. 21. SWPM 2012 21Deletion of a fact • Foundational: ▫ All implicit data that is no longer supported must also be deleted • Coherence: ▫ Delete implicit data only if it is necessary due to the deletion request
  22. 22. SWPM 2012 22 Example• Consider a KB containing the Starc P14 carried Laser scanning activities of Laser Scan Institute out by acquisition Acquisition P9 forms part of• Starc is propagated to all the subactivities of Laser Scan Detailed sequence Acquisition by rule R1 of shots• Update request: P14 Starc was not responsible for carried P9 Capture 1_1 out by forms part of• Two cases: Capture 1 Capture 10 •Actor disassociation (foundational) •Actor contraction (coherence)
  23. 23. 23• Update request: Starc was not responsible for Capture 1 Foundational Coherence P14 carried Laser P14 carried Laser Starc Starc scanning scanningInstitute out by Institute out by acquisition acquisition P9 forms P9 forms part of part of Detailed sequence Detailed sequence of shots of shots P14 P14 carried carried P9 forms P9 forms out by out by part of part of Capture 1 Capture 10 Capture 1 Capture 10 P14 carried out by P14 carried out by
  24. 24. SWPM 2012 24Complexity analysis • Similar operations can be also defined for rules R2 and R3
  25. 25. SWPM 2012 25Conclusion • Provenance-based Inference Rules ▫ We motivated the need for provenance-based inference rules to  reduce the storage space requirements  ease the ingestion of metadata and the error correction ▫ We identified three basic rules accompanied by real world examples. • Provenance-based Inference Rules and Knowledge Evolution ▫ The use of inference rules introduces difficulties with respect to the evolution of knowledge ▫ We identified two ways to deal with deletions in this context ▫ Even though we confined ourselves to CIDOC, and to three specific inference rules, the general ideas behind our work (including the discrimination between foundational and coherence semantics of deletion) can be applied to other models and/or sets of inference rules.
  26. 26. 26Thank you for your attention.
  27. 27. 27
  28. 28. SWPM 2012 28On repository policies Needs to be computed after every change 7 1 R: rdfs + rules 5 6 2 R: rdfs 4 3 R: rules 4 K 5 C: rdfs 2 3 6 C: rules 7 C: rdfs+rules 1 Increases space
  29. 29. SWPM 2012 29General statistics
  30. 30. SWPM 2012 30Space evaluation

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