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Using Schema Matching to Simplify Heterogeneous Data Translation.

Tova Milo, Sagit Zohar: Using Schema Matching to Simplify Heterogeneous Data Translation. VLDB 1998: 122-133
@inproceedings{DBLP:conf/vldb/MiloZ98,
  author    = {Tova Milo and
               Sagit Zohar},
  editor    = {Ashish Gupta and
               Oded Shmueli and
               Jennifer Widom},
  title     = {Using Schema Matching to Simplify Heterogeneous Data Translation},
  booktitle = {VLDB'98, Proceedings of 24rd International Conference on Very
               Large Data Bases, August 24-27, 1998, New York City, New York,
               USA},
  publisher = {Morgan Kaufmann},
  year      = {1998},
  isbn      = {1-55860-566-5},
  pages     = {122-133},
  ee        = {db/conf/vldb/MiloZ98.html},
  crossref  = {DBLP:conf/vldb/98},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}
BibTeX

Abstract

A broad spectrum of data is available on the Web in distinct heterogeneoussources, and stored under different formats. As the number of systems that utilize this heterogeneous data grows, the importance of data translation and conversion mechanisms increases greatly. In this paper we present a new translation system, based on schema-matching, aimed at simplifying the intricate task of data conversion. We observe that in many cases the schema of the data in the source system is very similar to that of the target system. In such cases, much of the translation work can be done automatically, based on the schemas similarity. This saves a lot of effort for the user, limiting the amount of programming needed. We define common schema and data models, in which schemas and data (resp.)from many common models can be represented. Using a rule-based method, the source schema is compared with the target one, and each component in the source schema is matched with a corresponding component in the target schema. Then, based on the matching achieved, data instances of the source schema can be translated to instances of the target schema. We show that our schema-based translation system allows a convenient specification and customization of data conversions, and can be easily combinedwith the traditional data-based translation languages.

Copyright © 1998 by the VLDB Endowment. Permission to copy without fee all or part of this material is granted provided that the copies are not made or distributed for direct commercial advantage, the VLDB copyright notice and the title of the publication and its date appear, and notice is given that copying is by the permission of the Very Large Data Base Endowment. To copy otherwise, or to republish, requires a fee and/or special permission from the Endowment.

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Printed Edition

Ashish Gupta, Oded Shmueli, Jennifer Widom (Eds.): VLDB'98, Proceedings of 24rd International Conference on Very Large Data Bases, August 24-27, 1998, New York City, New York, USA. Morgan Kaufmann 1998, ISBN 1-55860-566-5
Contents BibTeX

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Serge Abiteboul, Sophie Cluet, Tova Milo: Correspondence and Translation for Heterogeneous Data. ICDT 1997: 351-363 BibTeX
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Referenced by

  1. Chen Li, Edward Y. Chang: On Answering Queries in the Presence of Limited Access Patterns. ICDT 2001: 219-233
  2. Renée J. Miller, Laura M. Haas, Mauricio A. Hernández: Schema Mapping as Query Discovery. VLDB 2000: 77-88
  3. Yannis Papakonstantinou, Victor Vianu: DTD Inference for Views of XML Data. PODS 2000: 35-46
  4. Birgitta König-Ries: An Approach to the Semi-Automatic Generation of Mediator Specifications. EDBT 2000: 101-117
  5. Serge Abiteboul, Sophie Cluet, Tova Milo, Pini Mogilevsky, Jérôme Siméon, Sagit Zohar: Tools for Data Translation and Integration. IEEE Data Eng. Bull. 22(1): 3-8(1999)
  6. Tova Milo, Dan Suciu: Type Inference for Queries on Semistructured Data. PODS 1999: 215-226
  7. Catriel Beeri, Tova Milo: Schemas for Integration and Translation of Structured and Semi-structured Data. ICDT 1999: 296-313
  8. Daniela Florescu, Alon Y. Levy, Alberto O. Mendelzon: Database Techniques for the World-Wide Web: A Survey. SIGMOD Record 27(3): 59-74(1998)
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