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Data-driven understanding and refinement of schema mappings


Ling-Ling Yan, Renée J. Miller, Laura M. Haas, and Ronald Fagin

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Return to Schemas and Queries


Abstract

At the heart of many data-intensive applicatins is the problem of quickly and accuratly trassforming data into a new form. Database resarchers have long advocated the use of declarative queries for this process. Yet tools for creating, managing and understanding the complex queries necessary for data transformation are still too primitive to permit widespread adoptoin of this approach. We present a new fame work that uses data examples as the basis for understanding and refining declarative schema mappings. We identify a small set of intuitive operaots for manipulating examples. These operators permit a user to follow and refine an example by walking through a data source. We show that our operators are powerful enough both to identify a large class of schema mappings and to distighush effectivly between alternative schema mappings. These operators permit a user to quickly and intutivly build and rfine complex data trasformation queries that map one data source into another.


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