Exploring schema repositories with schemr

Authors:
Kuang Chen;Akshay Kannan;Jayant Madhavan;Alon Halevy
Affiliations:
University of California, Berkeley;University of California, Berkeley;Google, Inc.;Google, Inc.
Venue:
ACM SIGMOD Record
Year:
2011

Citing 9
Cited 1

Learning to Match the Schemas of Data Sources: A Multistrategy Approach

Machine Learning
A survey of approaches to automatic schema matching

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Corpus-Based Schema Matching

ICDE '05 Proceedings of the 21st International Conference on Data Engineering
Visualization of mappings between schemas

Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
From databases to dataspaces: a new abstraction for information management

ACM SIGMOD Record
Schema summarization

VLDB '06 Proceedings of the 32nd international conference on Very large data bases
WebTables: exploring the power of tables on the web

Proceedings of the VLDB Endowment
Exploring schema repositories with schemr

Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
OpenII: an open source information integration toolkit

Proceedings of the 2010 ACM SIGMOD International Conference on Management of data

Publish-time data integration for open data platforms

Proceedings of the 2nd International Workshop on Open Data

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Abstract

Schemr is a search engine for users to search for and visualize schemas in a metadata repository. Users may search by keywords and by example, using schema fragments as query terms. Schemr uses a novel search algorithm, based on a combination of text search and schema matching techniques, coupled with a structurally-aware scoring metric. Schemr presents search results in a GUI that allows users to explore which elements match and how well they do. The GUI supports interactions, including panning, zooming, layout and drilling-in. This paper introduces Schemr as a new component of the information integration toolbox and discusses its benefits in several applications.