research-article

Information preserving XML schema embedding

Authors:

Philip BohannonAuthors Info & Claims

ACM Transactions on Database Systems (TODS), Volume 33, Issue 1

Article No.: 4, Pages 1 - 44

https://doi.org/10.1145/1331904.1331908

Published: 21 March 2008 Publication History

Abstract

A fundamental concern of data integration in an XML context is the ability to embed one or more source documents in a target document so that (a) the target document conforms to a target schema and (b) the information in the source documents is preserved. In this paper, information preservation for XML is formally studied, and the results of this study guide the definition of a novel notion of schema embedding between two XML DTD schemas represented as graphs. Schema embedding generalizes the conventional notion of graph similarity by allowing an edge in a source DTD schema to be mapped to a path in the target DTD. Instance-level embeddings can be derived from the schema embedding in a straightforward manner, such that conformance to a target schema and information preservation are guaranteed. We show that it is NP-complete to find an embedding between two DTD schemas. We also outline efficient heuristic algorithms to find candidate embeddings, which have proved effective by our experimental study. These yield the first systematic and effective approach to finding information preserving XML mappings.

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Index Terms

Information preserving XML schema embedding
1. Computer systems organization
  1. Architectures
    1. Other architectures
      1. Heterogeneous (hybrid) systems
2. Information systems
  1. Data management systems

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cover image ACM Transactions on Database Systems

ACM Transactions on Database Systems Volume 33, Issue 1

March 2008

211 pages

ISSN:0362-5915

EISSN:1557-4644

DOI:10.1145/1331904

Issue’s Table of Contents

Copyright © 2008 ACM.

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Publication History

Published: 21 March 2008

Accepted: 01 October 2007

Revised: 01 March 2007

Received: 01 June 2006

Published in TODS Volume 33, Issue 1

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Chodpathumwan YTermehchy ARamsey SShrestha AGlen ALiu ZLi GLi ZIdreos SSrivastava D(2021)Structural Generalizability: The Case of Similarity SearchProceedings of the 2021 International Conference on Management of Data10.1145/3448016.3457316(326-338)Online publication date: 9-Jun-2021
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Zhang XShehata ABenes BAliaga D(2020)Automatic Deep Inference of Procedural Cities from Global-scale Spatial DataACM Transactions on Spatial Algorithms and Systems10.1145/34234227:2(1-28)Online publication date: 27-Oct-2020
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