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Using CrowdSourcingfor Data Analytics
Hector Garcia-Molina(work with Steven Whang, Peter Lofgren, Aditya Parameswaran and others)
Stanford University
• Big Data Analytics
• CrowdSourcing
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CrowdSourcing
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Real World Examples
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Image MatchingTranslation
Image MatchingTranslation
Categorizing ImagesCategorizing Images
Search RelevanceSearch Relevance
Data GatheringData Gathering
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Many Crowdsourcing Marketplaces!
Many Research Projects!
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4
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analytics
data
results
humans
Example tasks:• get missing data• verify results• analyze data
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analytics
data
results
humans
Example tasks:• get missing data• verify results• analyze data
Key Point:• use humans judiciously
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Today will illustrate with
• Entity Resolution
• (may cover another topic briefly)
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Traditional Entity Resolution
cleansing
analysis
System 1 System n...
what matches what??
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Why is ER Challenging?
• Huge data sets
• No unique identifiers
• Missing data
• Lots of uncertainty
• Many ways to skin the cat
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Simple ER Example
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Simple ER Example
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a
c
b
d
sim=0.9
sim=0.8
Simple ER Example
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a
c
b
d
sim=0.9
sim=0.8
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ER: Exact vs Approximate
products
camerasresolvedcameras
CDs
books
...
resolvedCDs
resolvedbooks
...
ER
ER
ER
Simple ER Algorithm
• Compute pairwise similarities
• Apply threshold
• Perform transitive closure
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Simple ER Algorithm
• Compute pairwise similarities
• Apply threshold
• Perform transitive closure
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0.45
0.630.5
0.9
0.950.9
0.7
0.9
0.87
0.7 0.4
0.6
0.5
0.63
Simple ER Algorithm
• Compute pairwise similarities
• Apply threshold
• Perform transitive closure
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0.9
0.950.9
0.7
0.9
0.87
0.7
threshold = 0.7
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Simple ER Algorithm
• Compute pairwise similarities
• Apply threshold
• Perform transitive closure
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0.9
0.950.9
0.7
0.9
0.87
0.7
Crowd ER
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Same as this?
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Crowd ER
• First Cut: For every pair of records,ask workers if they match (i.e., get similarity)
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Crowd ER
• First Cut: For every pair of records,ask workers if they match (i.e., get similarity)
• Too expensive!
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0.45
0.630.5
0.9
0.950.9
0.7
0.9
0.87
0.7 0.4
0.6
0.5
0.63
Crowd ER
• Second Cut: Compute similarities;workers verify "critical" pairs
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0.45
0.630.5
0.9
0.950.9
0.7
0.9
0.87
0.7 0.4
0.6
0.5
0.63
critical??
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Crowd ER
• Second Cut: Compute similarities;workers verify "critical" pairs
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0.45
0.630.5
0.9
0.950.9
0.7
0.9
0.87
0.7 0.4
0.6
0.5
0.63
critical??
Crowd ER
• Second Cut: Compute similarities;workers verify "critical" pairs
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0.45
0.630.5
0.9
0.950.9
0.7
0.9
0.87
0.7 0.4
0.6
0.5
0.63
critical??
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pairwise analysis
records
clusters
generate questions
Key Point:• use humans judiciously
global analysis
crowd
new evidence
Key Issue: Semantics of Crowd Answer
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Key Issue: Semantics of Crowd Answer
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?
EDC
B A
Also issue: Similarities as Probabilities
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sim(a,b) → prob(a,b)
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Strategy
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current state
a
b c
0.9
0.5
0.2
ER result use any given ER algorithm
Strategy
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current state
a
b c
0.9
0.5
0.2 Q(a,b)
Q(b,c)
Q(a,c)
consider ALL possible questions(three in this example)
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Strategy
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current state
a
b c
0.9
0.5
0.2 Q(a,b)
Q(b,c)
Q(a,c)
new state
new state
new state
new state
new state
new state
ER result
ER result
ER result
ER result
ER result
ER result
Y
Y
Y
N
N
N
considerpossibleoutcomes
Strategy
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current state
a
b c
0.9
0.5
0.2
Q(b,c)
new state ER resultY
example
a
b c
0.9
1.0
0.2a
b c
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Strategy
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current state
a
b c
0.9
0.5
0.2 Q(a,b)
Q(b,c)
Q(a,c)
new state
new state
new state
new state
new state
new state
ER result
ER result
ER result
ER result
ER result
ER result
score?
score?
score?
score?
score?
score?
Y
Y
Y
N
N
N
Two Remaining Issues
• How do we score an ER result?
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• Efficiency?
ER result gold standard
F score
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Gold Standard?
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a
b c
0.9
0.5
0.2a
b c
1.0
0.6
0.2
sim to prob
Gold Standard?
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a
b c
0.9
0.5
0.2a
b c
1.0
0.6
0.2
a
b c
a
b c
a
b c
a
b c
0.12
0.48
0.08
0.32
sim to prob
possible worlds
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Gold Standard?
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a
b c
0.9
0.5
0.2a
b c
1.0
0.6
0.2
a
b c
a
b c
a
b c
a
b c
0.12
0.48
0.08
0.32
a
b c
0.68
a
b c
0.32sim to prob
possible worlds possible clustering(via ER algorithm)
Strategy
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current state
a
b c
0.9
0.5
0.2 Q(a,b)
Q(b,c)
Q(a,c)
new state
new state
new state
new state
new state
new state
ER result
ER result
ER result
ER result
ER result
ER result
scorevs GS?Y
Y
Y
N
N
N
scorevs GS?
scorevs GS?
scorevs GS?
scorevs GS?
scorevs GS?
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Evaluating Efficiently
• See: Steven E. Whang, Peter Lofgren, and H. Garcia-Molina. Question Selection for Crowd Entity Resolution. To appear in Proc. 39th Int'l Conf. on Very Large Data Bases (PVLDB), Trento, Italy, 2013.
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Sample Result
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analytics
data
results
humans
Example tasks:• get missing data• verify results• analyze data
Key Point:• use humans judiciously
Summary
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analytics
bigdata
Now for something completely different!
DBMS
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analytics
bigdata
Now for something completely different!
DBMS
humans
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data
DeCo: Declarative CrowdSourcing
DBMS
humans
Enduser
what is best price forNikon DSLR cameras?
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data
DeCo: Declarative CrowdSourcing
DBMS
humans
Enduser
what is best price forNikon DSLR cameras?
model type brand
D7100 DSLR Nikon
7D DSLR Canon
P5000 comp Nikon
• • • • • • • • •
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data
DeCo: Declarative CrowdSourcing
DBMS
humans
Enduser
what is best price forNikon DSLR cameras?
model type brand
D7100 DSLR Nikon
7D DSLR Canon
P5000 comp Nikon
• • • • • • • • •
what is best price forNikon D7100 camera?
Crowd
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restaurant rating cuisine
Chez Panisse 4.9 French
Chez Panisse 4.9 California
Bytes 3.8 California
• • • • • • • • •
Example with a bit more detail:
Userview
restaurant rating cuisine
Chez Panisse 4.9 French
Chez Panisse 4.9 California
Bytes 3.8 California
• • • • • • • • •
⋈o
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Userview
restaurant
Chez Panisse
Bytes
• • •
restaurant rating
Chez Panisse 4.8
Chez Panisse 5.0
Chez Panisse 4.9
Bytes 3.6
Bytes 4.0
• • • • • •
restaurant cuisine
Chez Panisse French
Chez Panisse California
Bytes California
Bytes California
• • • • • •
• • • • • •
Anchor
Dependent Dependent
Example with a bit more detail:
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restaurant rating cuisine
Chez Panisse 4.9 French
Chez Panisse 4.9 California
Bytes 3.8 California
• • • • • • • • •
⋈o
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Userview
restaurant
Chez Panisse
Bytes
• • •
restaurant rating
Chez Panisse 4.8
Chez Panisse 5.0
Chez Panisse 4.9
Bytes 3.6
Bytes 4.0
• • • • • •
restaurant cuisine
Chez Panisse French
Chez Panisse California
Bytes California
Bytes California
• • • • • •
• • • • • •
Anchor
Dependent Dependent
fetch rule
fetch ruleBytes
Chez Panisse
fetch rule
Example with a bit more detail:
restaurant rating cuisine
Chez Panisse 4.9 French
Chez Panisse 4.9 California
Bytes 3.8 California
• • • • • • • • •
⋈o
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Userview
restaurant
Chez Panisse
Bytes
• • •
restaurant rating
Chez Panisse 4.8
Chez Panisse 5.0
Chez Panisse 4.9
Bytes 3.6
Bytes 4.0
• • • • • •
restaurant cuisine
Chez Panisse French
Chez Panisse California
Bytes California
Bytes California
• • • • • •
• • • • • •
Anchor
Dependent Dependent
fetch rule
fetch ruleBytes
Chez Panisse
fetch rulefetch rule
French
Example with a bit more detail:
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restaurant rating cuisine
Chez Panisse 4.9 French
Chez Panisse 4.9 California
Bytes 3.8 California
• • • • • • • • •
⋈o
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Userview
restaurant
Chez Panisse
Bytes
• • •
restaurant rating
Chez Panisse 4.8
Chez Panisse 5.0
Chez Panisse 4.9
Bytes 3.6
Bytes 4.0
• • • • • •
restaurant cuisine
Chez Panisse French
Chez Panisse California
Bytes California
Bytes California
• • • • • •
• • • • • •
Anchor
Dependent Dependent
resolutionrule
resolutionrule
Bytes
Chez Panisse
Example with a bit more detail:
restaurant rating cuisine
Chez Panisse 4.9 French
Chez Panisse 4.9 California
Bytes 3.8 California
• • • • • • • • •
⋈o
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Userview
restaurant
Chez Panisse
Bytes
• • •
restaurant rating
Chez Panisse 4.8
Chez Panisse 5.0
Chez Panisse 4.9
Bytes 3.6
Bytes 4.0
• • • • • •
restaurant cuisine
Chez Panisse French
Chez Panisse California
Bytes California
Bytes California
• • • • • •
• • • • • •
Anchor
Dependent Dependent
1. Fetch2. Resolve3. Join
Example with a bit more detail:
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Fetch[n]Fetch[ln]Fetch
[ln,c]Scan
D1(n,l)ScanA(n)
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Join
Join
AtLeast [8]SELECT n,l,cFROM countryWHERE l = ‘Spanish’ATLEAST 8
Resolve[m3]Resolve[d.e]
Fetch[nl]
ScanD2(n,c)
Resolve[m3]
Fetch[nl,c]
Many Query Processing Challenges
Filter [l=‘Spanish’]
Fetch[nl,c]
Deco Prototype V1.0
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Conclusion
• Crowdsourcing is important formanaging data!
• Still many challenges ahead!
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