1. Data Integration for the Relational Web
Michael J. Cafarella, Alon Halevy,
Nodira Khoussainova
Work done while at Google, Inc.
Presenter:
Michael J. Cafarella, University of Michigan
VLDB
August 27, 2009

2. Web Challenge
Try to create a database of all “VLDB program committee members”
Should be easy to obtain this dataset, as all the information exists on the Web
But unfortunately, data is:
Scattered across a dozen sites - User cannot know all of them in advance (Good luck finding the VLDB Cairo website!)
Not in XML, never intended for reuse
Transient integrations

3. Data Integration for Web
Can we combine tables to create new data sources?
Existing mashup, data integration tools ignore realities of Web data
A lot of useful data is not in XML
User cannot know all sources in advance
Transient integrations
Data semantics semi-tied to src page
Given the scope of data online, this should be HUGELY PROMISING AND SEDUCTIVE
Traditional database integration tools, we would map sources to a single designed “mediated schema” for every query!

4. Octopus Octopus Our test system has over 200M src tables
Name
Inst.
Year
Serge
Inria
1996
Michel
… Gren
Anton..
… Pisa
2005
Crawl Web
Extract Tables
Integrate Tables
Obtain Database
Lots of table/list-extraction work, e.g.,
[VLDB09, “Answering Table Augmentation…”, Gupta & Sarawagi]
[JAIR08, “Creating relational data…”, Michelson & Knoblock]
[WWW07, “Towards domain-independent…”, Gatterbauer et al]
[WWW02, “A machine learning based…”, Wang & Hu]
Octopus
Our test system has over 200M src tables
Our system uses data from:
WebTables [WebDB08, “Uncovering…”, Cafarella et al] [VLDB08, “WebTables: Exploring…”, Cafarella et al]
Harvesting Relational Tables from Lists [VLDB09, “Harvesting Relational Tables from Lists…”, Elmeleegy et al]
Lots of tabular-extraction work, e.g.,
[VLDB09, “Answering Table Augmentation…”, Gupta & Sarawagi]
[WWW07, “Towards domain-independent…”, Gatterbauer et al]
[WWW02, “A machine learning based…”, Wang & Hu] …

5. Outline Introduction Data Sources Octopus Operators
SEARCH
CONTEXT
EXTEND
Algorithms & Experiments
Conclusions

6. Outline Introduction Data Sources Octopus Operators
SEARCH
CONTEXT
EXTEND
Algorithms & Experiments
Conclusions

8. List Extraction

9. List Extraction What’s Opera Doc Warner 1957 Duck Amuck 1953
The Band Concert
Disney
1935
Duck Dodgers…
One Froggy Evening
1956 …

10. Outline Introduction Data Sources Octopus Operators
SEARCH
CONTEXT
EXTEND
Algorithms & Experiments
Conclusions

11. Octopus
Provides “workbench” of data integration operators to build target database
Most operators are not correct/incorrect, but high/low quality
Some prosaic operators: project, select, …
Three original operators
SEARCH
CONTEXT
EXTEND
Under covers, each operator recovers different aspect of implicit GLAV src desc.
Each operator can be thought of as recovering a different aspect of an implicit set of GLAV source descriptions.
These source descriptions are never explicitly shown to the user, but are revealed by the interaction of the user and the data.

12. Operator #1 - SEARCH SEARCH(“VLDB program committee members”)
serge abiteboul
inria
michael adiba
…grenoble
antonio albano
…pisa …
RANK of CLUSTERS. Here, a cluster of two tables.
------
Each cluster returned by SEARCH corresponds to a mediated schema relation in the GLAV representation.
Each member table of a cluster is a concrete table that contributes to the cluster’s relation. (and are unioned together)
User can perform SELECT PROJECT UNION integrations, plus some limited JOINs.
serge abiteboul
inria
anastassia ail…
carnegie…
gustavo alonso
etz zurich …

13. Operator #2 - CONTEXT Recover relevant data CONTEXT() CONTEXT()
serge abiteboul
inria
michael adiba
…grenoble
antonio albano
…pisa …
CONTEXT()
serge abiteboul
inria
anastassia ail…
carnegie…
gustavo alonso
etz zurich …
CONTEXT()

14. Operator #2 - CONTEXT Recover relevant data CONTEXT() CONTEXT()
serge abiteboul
inria
1996
michael adiba
…grenoble
antonio albano
…pisa …
CONTEXT()
CONTEXT operates on a single table.
In the GLAV description, CONTEXT is equivalent to
figuring out the selection predicates that apply to the mapping between the source table and a mediated table.
Here, for example, we figure out that one table effectively has a year=1996 predicate and another has year=2005.
This information is only available via the source page’s embedding web page. CONTEXT makes it explicit.
serge abiteboul
inria
2005
anastassia ail…
carnegie…
gustavo alonso
etz zurich …
CONTEXT()

15. Prosaic Operator - Union
Combine datasets
serge abiteboul
inria
1996
michael adiba
…grenoble
antonio albano
…pisa …
serge abiteboul
inria
1996
michael adiba
…grenoble
antonio albano
…pisa
2005
anastassia ail…
carnegie…
gustavo alonso
etz zurich …
Union()
serge abiteboul
inria
2005
anastassia ail…
carnegie…
gustavo alonso
etz zurich …

16. Operator #3 - EXTEND Add column to data
Similar to “join” but join target is a topic
EXTEND( “publications”, col=0)
“publications”
serge abiteboul
inria
1996
“Large Scale P2P Dist…”
michael adiba
…grenoble
“Exploiting bitemporal…”
antonio albano
…pisa
“Another Example of a…”
2005
anastassia ail…
carnegie…
“Efficient Use of the…”
gustavo alonso
etz zurich
“A Dynamic and Flexible…” …
serge abiteboul
inria
1996
michael adiba
…grenoble
antonio albano
…pisa
2005
anastassia ail…
carnegie…
gustavo alonso
etz zurich …
EXTEND modifies a GLAV description to contain another table and join key
(((Union of remaining tables in cluster yields single relation
Contains 243 tuples (223 completely correct)
Drawn from five sources across three SIGMOD years (and three websites))))

17. Straightforward Sequence
SEARCH(“VLDB program committee members”)
CONTEXT
serge abiteboul
inria
michael adiba
…grenoble
antonio albano
…pisa …
CONTEXT
serge abiteboul
inria
anastassia ail…
carnegie…
gustavo alonso
etz zurich …

18. Straightforward Sequence
CONTEXT
union
serge abiteboul
inria
1996
michael adiba
…grenoble
antonio albano
…pisa …
CONTEXT
serge abiteboul
inria
2005
anastassia ail…
carnegie…
gustavo alonso
etz zurich …

19. Straightforward Sequence
EXTEND
union
serge abiteboul
inria
1996
“Large Scale P2P Dist…”
michael adiba
…grenoble
“Exploiting bitemporal…”
antonio albano
…pisa
“Another Example of a…”
2005
anastassia ail…
carnegie…
“Efficient Use of the…”
gustavo alonso
etz zurich
“A Dynamic and Flexible…” …
serge abiteboul
inria
1996
michael adiba
…grenoble
antonio albano
…pisa
2005
anastassia ail…
carnegie…
gustavo alonso
etz zurich …
User integrated data sources with 4 operations
No wrappers; data was never intended for reuse
User never visited source web pages

20. Outline Introduction Data Sources Octopus Operators
SEARCH
CONTEXT
EXTEND
Algorithms & Experiments
Conclusions

21. Experiments
~50 queries, suggested and evaluated by Amazon Mechanical Turk
Query load of ~50 queries, suggested and evaluated by Amazon Mechanical Turk

22. SEARCH Algorithms - Ranking
SimpleRank - search engine ranking
SCPRank - symmetric conditional probability between query, table data
Similar to Pointwise Mutual Information
[Lopes, DaSilva, 1999], multiword units
Unfortunately, you can have very relevant tables that do not have a text hit on the query
Informally, measures correlation between query and each table term; find max of column-sums
Cite: Sixth meeting on Mathematics of Language
Max of per-column sums
SCPRank is very computationally burdensome, so we very roughly approximate it

23. SEARCH Algorithms - Ranking
Top-2
Top-5
Top-10
SimpleRank
27%
51%
73%
SCPRank
47%
64%
81%
Informally, measures correlation between query and each table term; find max of column-sums
SCPRank is very computationally burdensome, so we very roughly approximate it
Cite: Sixth meeting on Mathematics of Language

24. SEARCH Algorithms - Ranking
Top-2
Top-5
Top-10
SimpleRank
27%
51%
73%
SCPRank
47%
64%
81%
See paper for clustering results
Substantial gains possible beyond default web search relevance (as shown in our paper last VLDB).

25. CONTEXT Algorithms Input: table and source page
Output: data values to add to table
SignificantTerms sorts terms in source page by “importance” (tf-idf)
On the VLDB site, the conerence name, year, and location are in the surrounding text.
The data itself contains a person name and that person’s institution.

26. Related View Partners Looks for different “views” of same data
Consider the VLDB conference page and a PC member’s home page
Find tables elsewhere on Web that contain values from SignificantTerms
But on a researcher’s home page, the data table probably contains a set of PC memberships,
Listing the conference-name, year, and place. The researcher’s name and institution are probably in the surrounding text.
So RVP works by looking for source terms that are matched a LOT by other tables on the Web.

27. CONTEXT Experiments
Here, on a query load of ~50 queries, we measure the percentage of queries that yield a good CONTEXT value in the top-1 returned by the system, the top-2, the top-3, etc.
Y-axis is the percentage of tables that yielded a GOOD context term within the top-k
BLUE is SignificantTerms
RED is the RelatedViewPartners
GREEN is a hybrid algorithm

28. EXTEND Algorithms Input: src table, src column, dst topic JoinTest:
EXTEND(t, col=0, “publications”)
JoinTest:
Tests a single table for join-compatibility
“City mayors”: yes
“VLDB publications”: no
Rank all tables by relevance to query topic
Select tables that are joinable to query column
MultiJoin
Finds a join-target tuple for each src tuple
“City mayors”: maybe
“VLDB publications”: yes
For each cell in src column, perform topic search
Cluster resulting tables, rank by column coverage
JoinTest uses search engine ranking for relevance
Joinability-test is performed by computing Jaccard score between the set of items in query column and the set of items in tested join-column.
If jaccard score passes threshold (0.8 I believe),
-- Strict item equality not required. It’s a string-edit-distance threshold test.
Multijoin

29. EXTEND Early Experiments
JoinTest
3 of 7 source tables
60% of source tuples
Single extension for each extended tuple
MultiJoin
All 7 source tables
33% of source tuples
Avg 45.5 extensions for each extended tuple
113 NYC mayors
12 albums by Led Zeppelin
Join Column
Topic Query
countries
universities
Us states
governors
Us cities
mayors
Film titles
characters
UK political parties MP
Baseball teams
players
Musical bands
albums
Not many of our source queries are actually EXTENDable. Just 7….
MultiJoin and JoinTest should probably be separate operators. OR, perhaps we could automatically choose one based on source data.
BUT, they are not really competitive - they apply in different situations, depending on the nature of the data.

30. Related Work Octopus relies on info extraction work
Substantial work in data integration
Mashup Tools
Yahoo! Pipes
Marmite - [Wong and Hong, 2007]
Karma - [Tuchinda, et al., 2007]
CIMPLE - [DeRose, et al., 2007]
Potter’s Wheel - [Raman and Hellerstein, 2001]
Yahoo Pipes allows user to easily pipe togther XML flows, but assumes structured data inputs (and that user can find them)
Karma populates a user’s db, but requires sources with formal declarations
Cimple tries the Web Integration project, but still requires a lot of manual work by an administrator. Not intended for transient integrations, but rather long-lasting ones that are easy to maintain (however still relatively burdensome to create)
Potter’s Wheel emphasizes live interaction for data cleaning. Its workbench-style interface is the closest to the Octopus model

31. Octopus Contributions
Basic operators that enable Web data integration with very small user burden
Realistic and useful implementations for all three operators
Future work:
Efficient large-scale implementation
Some serious challenges for performance, esp. for items that issue a huge number of Web queries (the MultiJoin algorithm) or that use a lot of non-adjancent word statistics from the web (as in the SCP-ranking function).
Not sure what the efficiency/accuracy tradeoff is yet.