Clip: A tool for mapping hierarchical schemas

Alessandro Raffio, Daniele Braga, Stefano Ceri, Paolo Papotti, Mauricio A. Hernández

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Citations (Scopus)

Abstract

Many data integration solutions in the market today include visual tools for schema mapping. Users connect schema elements with lines that are interpreted as high-level logical expressions capturing the relationship between source and target data-sets. These expressions are compiled into queries or programs that convert source-side data instances into target-side instances. In this demo we showcase Clip, an XML Schema mapping tool. Clip is distinguished from existing tools in that mappings explicitly specify structural transformations in addition to value correspondences. We show how Clip's users enter mappings by drawing lines and how these lines are translated into XQuery.

Original languageEnglish
Title of host publicationSIGMOD 2008
Subtitle of host publicationProceedings of the ACM SIGMOD International Conference on Management of Data 2008
Pages1271-1274
Number of pages4
DOIs
Publication statusPublished - 10 Dec 2008
Event2008 ACM SIGMOD International Conference on Management of Data 2008, SIGMOD'08 - Vancouver, BC, Canada
Duration: 9 Jun 200812 Jun 2008

Publication series

NameProceedings of the ACM SIGMOD International Conference on Management of Data
ISSN (Print)0730-8078

Conference

Conference2008 ACM SIGMOD International Conference on Management of Data 2008, SIGMOD'08
CountryCanada
CityVancouver, BC
Period9/6/0812/6/08

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Keywords

  • Design
  • Experimentation
  • Languages

ASJC Scopus subject areas

  • Software
  • Information Systems

Cite this

Raffio, A., Braga, D., Ceri, S., Papotti, P., & Hernández, M. A. (2008). Clip: A tool for mapping hierarchical schemas. In SIGMOD 2008: Proceedings of the ACM SIGMOD International Conference on Management of Data 2008 (pp. 1271-1274). [1376753] (Proceedings of the ACM SIGMOD International Conference on Management of Data). https://doi.org/10.1145/1376616.1376753