Learning to create data-integrating queries

  • Authors:

  • Partha Pratim Talukdar;Marie Jacob;Muhammad Salman Mehmood;Koby Crammer;Zachary G. Ives;Fernando Pereira;Sudipto Guha

  • Affiliations:

  • University of Pennsylvania, Philadelphia, PA;University of Pennsylvania, Philadelphia, PA;University of Pennsylvania, Philadelphia, PA;University of Pennsylvania, Philadelphia, PA;University of Pennsylvania, Philadelphia, PA;University of Pennsylvania, Philadelphia, PA;University of Pennsylvania, Philadelphia, PA

  • Venue:
  • Proceedings of the VLDB Endowment
  • Year:
  • 2008

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Abstract

The number of potentially-related data resources available for querying --- databases, data warehouses, virtual integrated schemas --- continues to grow rapidly. Perhaps no area has seen this problem as acutely as the life sciences, where hundreds of large, complex, interlinked data resources are available on fields like proteomics, genomics, disease studies, and pharmacology. The schemas of individual databases are often large on their own, but users also need to pose queries across multiple sources, exploiting foreign keys and schema mappings. Since the users are not experts, they typically rely on the existence of pre-defined Web forms and associated query templates, developed by programmers to meet the particular scientists' needs. Unfortunately, such forms are scarce commodities, often limited to a single database, and mismatched with biologists' information needs that are often context-sensitive and span multiple databases. We present a system with which a non-expert user can author new query templates and Web forms, to be reused by anyone with related information needs. The user poses keyword queries that are matched against source relations and their attributes; the system uses sequences of associations (e.g., foreign keys, links, schema mappings, synonyms, and taxonomies) to create multiple ranked queries linking the matches to keywords; the set of queries is attached to a Web query form. Now the user and his or her associates may pose specific queries by filling in parameters in the form. Importantly, the answers to this query are ranked and annotated with data provenance, and the user provides feedback on the utility of the answers, from which the system ultimately learns to assign costs to sources and associations according to the user's specific information need, as a result changing the ranking of the queries used to generate results. We evaluate the effectiveness of our method against "gold standard" costs from domain experts and demonstrate the method's scalability.