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Advancements in Data Integration for the Relational Web: The Octopus Framework

This presentation by Michael J. Cafarella delves into the challenges and methodologies of data integration from web sources. Conducted at VLDB 2009, it showcases the Octopus system, which enables effective data extraction and integration from diverse online tables, focusing on non-XML data sources. Key topics include the development of user-friendly operators such as SEARCH, CONTEXT, and EXTEND, and the importance of addressing transient integrations in the web's complex data landscape. Discover the algorithms and experiments that lay the groundwork for enhanced web data integration.

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Advancements in Data Integration for the Relational Web: The Octopus Framework

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  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”

  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

  4. Octopus • 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] Crawl Web Extract Tables Integrate Tables Obtain Database • Octopus • Our test system has over 200M src tables • 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]

  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

  7. List Extraction

  8. List Extraction

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

  10. 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.

  11. Operator #1 - SEARCH • SEARCH(“VLDB program committee members”)

  12. Operator #2 - CONTEXT • Recover relevant data CONTEXT() CONTEXT()

  13. Operator #2 - CONTEXT • Recover relevant data CONTEXT() CONTEXT()

  14. Prosaic Operator - Union • Combine datasets Union()

  15. Operator #3 - EXTEND • Add column to data • Similar to “join” but join target is a topic “publications” EXTEND( “publications”, col=0)

  16. Straightforward Sequence • SEARCH(“VLDB program committee members”) • CONTEXT • CONTEXT

  17. Straightforward Sequence union • CONTEXT • CONTEXT

  18. Straightforward Sequence union • EXTEND • User integrated data sources with 4 operations • No wrappers; data was never intended for reuse • User never visited source web pages

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

  20. Experiments • ~50 queries, suggested and evaluated by Amazon Mechanical Turk

  21. 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

  22. SEARCH Algorithms - Ranking

  23. SEARCH Algorithms - Ranking • See paper for clustering results

  24. CONTEXT Algorithms • Input: table and source page • Output: data values to add to table • SignificantTerms sorts terms in source page by “importance” (tf-idf)

  25. Related View Partners • Looks for different “views” of same data

  26. CONTEXT Experiments

  27. EXTEND Algorithms • Input: src table, src column, dst topic • 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

  28. 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

  29. 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]

  30. 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

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