anhai doan alon halevy zachary ives chapter 14: data provenance principles of data integration
TRANSCRIPT
ANHAI DOAN ALON HALEVY ZACHARY IVES
CHAPTER 14: DATA PROVENANCE
PRINCIPLES OF
DATA INTEGRATION
“Where Did this Data Come from?”
Challenge: integrated data may come from many sources and mappings – of different quality or trustworthiness!
How did I get this particular result? What mappings produced it? How much should I trust (believe) it?
Data provenance (lineage) captures the relationships between tuples in a set of data instances
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An Example: View Tuple Derivations
B C
2 3
3 2
4 3
A B
1 2
2 4
RR SS
Source relations
A C directly derivable by
1 3 R(1,2) ⋈ S(2,3) ∪ R(1,4) ⋈ S(4,3)
2 2 S(2,3) ⋈ ρB A, C B S(3,2)
3 3 S(3,2) ⋈ ρB A, C B S(2,3)
View V1 = R ⋈ S ∪ S ⋈ S
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Formulating a Provenance Model
Conceptually, provenance captures the operations and operands going into a resultThere are many options to do this, and many levels of detail!
A “good” provenance model should: Have a formal semantics Have equivalence properties such that equivalent query
plans produce equivalent provenance Connect to notions of value, quality or score
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Outline
The two views of provenance Applications of data provenance Provenance semirings: one ring to rule them all Storing provenance
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Provenance as Annotations on Data
Annotate each derivation with an “explanation” in terms of relational algebra and the tuple operands
Lets us “look up” the derivation of a result
B C
2 3
3 2
4 3
A B
1 2
1 4
RR
SSA C provenance annotation
1 3 R(1,2) ⋈ S(2,3) ∪ R(1,4) ⋈ S(4,3)
2 2 S(2,3) ⋈ ρB A, C B S(3,2)
3 3 S(3,2) ⋈ ρB A, C B S(2,3)
View V1 (in Datalog):V1(x,z) :- R(x,y), S(y,z)V1(x,x) :- S(x,y), S(y,x)
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Provenance as a Graph of Relationships
Bipartite graph: tuple nodes connected via “derivation nodes” Encodes a hypergraph (hyperedges = derivations)
Makes direct derivation relationships more explicit
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R(1,2)
R(1 ,4)
S(2,3)
S(3,2)
S(4,3)
V1(1,3)
V1(2,2)
V1(3,3)
derives via V1
derives via V1
derives via V1
derives via V1
Making the Two Interchangeable
We can make these equivalent by introducing provenance tokens (equiv. node IDs) for each tuple Derived tuples’ annotations = expressions over tokens
B C ann
2 3 s1
3 2 s2
4 3 s3
A B ann
1 2 r1
1 4 r2
RR
SS A C ann
1 3 v1 = r1 ⋈ s1 ∪ r2 ⋈ s3
2 2 v2 = s1 ⋈ s2
3 3 v3 = s2 ⋈ s1
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VV11
r1
r2
s1
s2
s3
v1
v2
v3
V1
V1
V1
V1
Outline
The two views of provenance Applications of data provenance Provenance semirings: one ring to rule them all Storing provenance
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Where Can We Use Provenance?
Explanations Help the user understand why an item exists
Scoring Provide a ranked list of “most relevant” results
Reasoning about interactions Help the user understand data relationships
Examples of Provenance’s Utility
Schema mapping debugging:We may have a bad result
Determine why that result exists, what is faulty
Bioinformatics data integration:Different sources have different levels of reliability or authoritativeness
Rank results by score!
Probabilistic databases:We may need to know that results are correlated
Encode the relationships, use to assign probabilities
Outline
The two views of provenanceApplications of data provenance Provenance semirings: one ring to rule them all Storing provenance
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The Notion of Provenance as Annotations
Many formalisms were defined for using query computations to produce annotations
Each captured certain subtleties
The key question: Is there one “most powerful” model that captures the properties of the relational algebra*? Equivalent queries should produce equivalent provenance
* over multi-sets or bags, as used by “real” systems
The Provenance Semiring Model
To represent provenance, use: A set of provenance tokens or tuple IDs, K
Abstract operators representing combination of tuplesAbstract sum operator, ⊕, for union or projection
has identity element 0 (a ⊕ 0 ≡ 0 ⊕ a ≡ 0)
Abstract product operator, ⊗, for join has identity element 1 (a ⊗ 1 ≡ 1 ⊗ a ≡ 1) also (a ⊗ 0 ≡ 0 ⊗ a ≡ 0)
This is formally a commutative semiring
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The Provenance Semiring Model
We can re-express our example as below, using the semiring operators instead of the relational algebra ones
B C ann
2 3 s1
3 2 s2
4 3 s3
A B ann
1 2 r1
1 4 r2
RR
SS A C Ann
1 3 v1 = r1 ⊗ s1 ⊕ r2 ⊗ s3
2 2 v2 = s1 ⊗ s2
3 3 v3 = s2 ⊗ s1
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VV11
r1
r2
s1
s2
s3
v1
v2
v3
V1
V1
V1
V1
Tokens for Mappings
Sometimes we would like to assign a token to the actual mapping or rule used – so we can assign it a value
B C ann
2 3 s1
3 2 s2
4 3 s3
A B ann
1 2 r1
1 4 r2
RR
SS A C Ann
1 3 v1 = m1⊗ [r1 ⊗ s1] ⊕ m2⊗ [r2 ⊗ s3]
2 2 v2 = m2⊗ [s1 ⊗ s2]
3 3 v3 = m2⊗ [s2 ⊗ s1]16
VV11
View V1 (in Datalog):V1(x,z) :- R(x,y), S(y,z)V1(x,x) :- S(x,y), S(y,x)
Call this m1
Call this m2
Example Application: Provenance Visualization
Base tuple derivation(token not shown)
Tuple nodes
Derivation bymapping M5
Example Application: Tuple Scoring For ranked query results, we may adopt the following model commonly used in
ranking: Assign a score to each base tuple = - log2(probability) Use arithmetic sum as ⊗ Use min as ⊕
Suppose prob(r1) = 0.5, prob(s1) = 0.5, others are 1.0
A C Ann
1 3 v1 = r1 ⊗ s1 ⊕ r2 ⊗ s3
= min((2+1),(1+1)) = 2
2 2 v2 = s1 ⊗ s2 = 2+1 = 3
3 3 v3 = s2 ⊗ s1 = 1+2 = 3
VV11
Useful Semirings
Use case Base value Product R ⊗ S
Sum R ⊕ S
Derivability True R ∧ S R ∨ S
Trust Trust condition result
R ∧ S R ∨ S
Confidentiality level
Tuple confidentiality
level
More_secure(R,S)
Less_secure(R,S)
Weight / cost Base tuple weight
R + S min(R,S)
Lineage Tuple ID R ∪ S R ∩ S
Probabilistic event
Tuple probabilistic
event
R ∧ S R ∨ S
Number of derivations
1 R ⋅ S R + S19
Outline
The two views of provenanceApplications of data provenanceProvenance semirings: one ring to rule them all Storing provenance
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Storing Provenance Use tuple keys as tokens Encode provenance graph as relations
B C
2 3
3 2
4 3
A B
1 2
1 4
RR
SS
A C
1 3
2 2
3 3
VV11
View V1 (in Datalog):V1(x,z) :- R(x,y), S(y,z)V1(x,x) :- S(x,y), S(y,x)
Relate tuples with table Pv1-1
Relate tuples with table Pv1-2
R.A R.B S. B
S.C V1.A
V1.C
1 2 2 3 1 3
1 4 4 3 1 3
S.B S.C S.B’
S.C’
V1.A
V1.C
2 3 3 2 2 2
3 2 2 3 3 3 21
PPv1-1v1-1
PPv1-2v1-2
Storing Provenance Use tuple keys as tokens Encode provenance graph as relations
B C
2 3
3 2
4 3
A B
1 2
1 4
RR
SS
A C
1 3
2 2
3 3
VV11
View V1 (in Datalog):V1(x,z) :- R(x,y), S(y,z)V1(x,x) :- S(x,y), S(y,x)
R.A R.B S. B
S.C V1.A
V1.C
1 2 2 3 1 3
1 4 4 3 1 3
S.B S.C S.B’
S.C’
V1.A
V1.C
2 3 3 2 2 2
3 2 2 3 3 3 22
PPv1-1v1-1
PPv1-2v1-2
These are redundantif we know the Datalog
Storing Provenance Use tuple keys as tokens Encode provenance graph as relations
B C
2 3
3 2
4 3
A B
1 2
1 4
RR
SS
A C
1 3
2 2
3 3
VV11
View V1 (in Datalog):V1(x,z) :- R(x,y), S(y,z)V1(x,x) :- S(x,y), S(y,x)
A B C
1 2 3
1 4 3
B C C’
2 3 2
3 2 323
PPv1-1v1-1
PPv1-2v1-2
Data Provenance Wrap-up
Provenance is critical to understanding and assessing the believability of data, and in debugging Two equivalent representations – annotations vs graph Provenance semiring model preserves the “expected”
equivalences of the relational algebra We can take semiring provenance and evaluate it with different
semirings to get useful scores We can store provenance using relations
Recent work beyond the scope of the book: Extending provenance to more complex queries, e.g., with
aggregation Languages for querying provenance (primarily as a graph)