Transcript Slide - ProvenanceWeek

Approximated Provenance for
Complex Applications
Susan B. Davidson
University of Pennsylvania
Eleanor Ainy, Daniel Deutch, Tova Milo
Tel Aviv University
Crowd Sourcing
The engagement of crowds of
Web users for data procurement
and knowledge creation.
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Why now?
We are all connected, all the time!
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Complexity?
• Many of the initial applications were quite
simple
– Specify Human Interaction Task (HIT) using e.g.
Mechanical Turk, collect responses, aggregate to
form result.
• Newer ideas are multi-phase and complex,
e.g. mining frequent fact sets from the crowd
(OASSIS)
– Model as workflows with global state
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Outline
• “State-of-the-art” in crowd data provenance
• New challenges
• A proposal for modeling crowd data
provenance
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Outline
• “State-of-the-art” in crowd data provenance
• New challenges
• A proposal for modeling crowd data
provenance
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Crowd data provenance?
• TripAdvisor: aggregates reviews and presents
average ratings
– Individual reviews are part of the provenance
• Wikipedia: keeps extensive information about
how pages are edited
– ID of the user who generated the page as well as
changes to page (when, who, summary)
– Provides several views of this information, e.g. by
page or by editor
• Mainly used for presentation and explanation
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Outline
• “State-of-the-art” in crowd data provenance
• New challenges
• A proposal for modeling crowd data
provenance
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Challenges for crowd data provenance
• Complexity of processes and number of user
inputs involved
– Provenance can be very large, leading to difficulties in
viewing and understanding provenance
• Need for
–
–
–
–
Summarization
Multidimensional views
Provenance mining
Compact representation for maintenance and
cleaning
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Summarization
• Large size of provenance  need for abstraction
– E.g., in heavily edited Wikipedia pages:
• “x1, x2, x3 are formatting changes; y1, y2, y3, y4 add
content; z1 , z2 represent divergent viewpoints”
• “u1 , u2 , u3 represent edits by robots; v1, v2 represent edits
by Wikipedia administrators”
– E.g., in a movie-rating application to summarize the
provenance of the average rating for “MatchPoint”
• “Audience crowd members gave higher ratings (8-10)
whereas critics gave lower ratings (3-5).”
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Multidimensional Views
• “Perspective” through which provenance can
be viewed or mined
– E.g. in TripAdvisor, if there is an “outlier” review it
would be useful to see other reviews by that
person to “calibrate” it.
– “Question” perspective could show which
questions are bad/unclear
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Maintenance and Cleaning
• May need update propagation to remove
certain users, questions and/or answers
– E.g. spammers or bad questions
• Mining of provenance may lag behind the
aggregate calculation
– E.g., detecting a spammer may only be possible
when they have answered enough questions, or
when enough answers have been obtained from
other users.
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Outline
• “State-of-the-art” in crowd data provenance
• New challenges
• A proposal for modeling crowd data
provenance
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Crowd Sourcing Workflow
Movie reviews Aggregator Platform
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Provenance expression
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Propagating provenance annotations through
joins
R
A
B
C
R
…
a b c p
…
JOIN (on B)
S
D
B
A
E
…
d b e r
…
B
⋈S
C
D E
…
a b c d e p*r
…
The annotation p * r means
joint use of data annotated by
p and data annotated by r
[Green, Karvounarakis, Tannen, Provenance Semirings. PODS 2007]
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Propagating provenance annotations through
unions
and
projections
R
A
B
C
…
a b c1 p
…
a b c2 r
…
a b c3 s
…
πABR
A
B
…
PROJECT
a b p+r+s
…
+ means alternative use of
data, which arises in both PROJECT
and UNION.
[Green, Karvounarakis, Tannen, Provenance Semirings. PODS 2007]
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Annotated Aggregate Expressions
Q=
R
select Dept, sum(Sal)
from R
group by Dept
Eid Dept
Sal
1 d1 20 p1
2 d1 10 p2
3 d1 15 P3
The sum salary for d1 could be represented
by the expression
(20 ⊗p1 + 10 ⊗
p2 + 15⊗p3)
This provenance aware value “commutes” with deletion.
[Amsterdamer, Deutch, Tannen, Provenance for Aggregate Queries. PODS 2011]
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Provenance expression
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Provenance expression: Benefits
• Can understand how movie ratings were
computed.
• Can be used for data maintenance and
cleaning
– E.g. if U2 is discovered to be a spammer, “map” its
provenance annotation to 0
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Summarizing provenance
• Map annotations to a corresponding
“summary”
– h: Ann  Ann’, where |Ann’| << |Ann|
• E.g. in our example, let
– h(Ui)=h(Si)=1, h(Ai)=A, h(Ci)=C
– Reducing the expression to
– Which simplifies to
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Constructing mappings?
• How do we define and find “good” mappings?
– Provenance size
– Semantic constraints (e.g. two annotations can
only be mapped to the same annotation if they
come from the same input table)
– Distance between original provenance expression
and the mapped expression (e.g. grouping all
young French people and giving them an average
rating for some movie)
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Conclusions
• Provenance is needed for crowd-sourcing
applications to help understand the results
and reason about their quality.
• Techniques from database/workflow
provenance can be used, but there are special
challenges and “opportunities”
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