This document discusses evolving workflow provenance information in the presence of custom inference rules. It presents three inference rules for provenance data, including that actors are associated with all subactivities if one activity, objects and their parts are used together, and information objects are present where physical objects carrying them are. It examines handling updates to provenance knowledge bases using these rules either by deleting all inferred facts or only as needed, and considers complexity of different approaches.

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.gr
Institute of Computer Science
Foundation for Research SWPM Workshop
and Technology – Hellas 28/05/2012
ICS-FORTH Crete, Herakleion
Computer Science Department
University of Crete

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Outline
• Provenance-based inference rules
• Knowledge evolution of provenance information

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What 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.

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Why 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?

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Motivation
• 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

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Storage Space Challenge
Extra data for
Adoption of Inference
provenance
Storage mechanisms
Space
Data
Production
Time

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User Input Challenge
Record
Provenance
Digital
Metadata Ingestion
RDF/S
Repository

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User Input Challenge
Day 1 Day 11111
……………. Adoption of
Inference
mechanisms
Time

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Error Correction Challenge
Scientific workflow Provenance Data Metadata
record ingestion
Which
one to
correct?
From
where to Adoption of Inference
start mechanisms
searching?

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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

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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 E24
Small part of P16 was used
carried Physical
CIDOC CRM for
out by Man-Made
Thing
E22 Man-made IsA
E39 Actor Object (Device)
P46 forms part of

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The three inference rules
R1:
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 for
that event.
R3:
If a physical object that carries an information object
was present at an event, then that information object
was also present at the event.

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3d Reconstruction process

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Actors - 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

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Devices - 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

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Information 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

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Outline
• Provenance-based inference rules
• Knowledge evolution of provenance information

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Knowledge 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

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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

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Foundational 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

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Deletion 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

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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)

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• Update request:
Starc was not responsible for Capture 1
Foundational Coherence
P14 carried Laser P14 carried Laser
Starc Starc
scanning scanning
Institute 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

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Complexity analysis
• Similar operations can be also defined for
rules R2 and R3

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Conclusion
• 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.

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Thank you for your attention.

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On 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

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General statistics

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Space evaluation