Approximate graph schema extraction for semi-structured data
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AbstractSemi-structured data are typically represented in the form of labeled directed graphs. They are self-describing and schemaless. The lack of a schema renders query processing over semi-structured data expensive. To overcome this predicament, some researchers proposed to use the structure of the data for schema representation. Such schemas are commonly referred to as graph schemas. Nevertheless, since semistructured data are irregular and frequently subjected to modifications, it is costly to construct an accurate graph schema and worse still, it is difficult to maintain it thereafter. Furthermore, an accurate graph schema is generally very large, hence impractical. In this paper, an approximation approach is proposed for graph schema extraction. Approximation is achieved by summarizing the semi-structured data graph using an incremental clustering method. The preliminary experimental results have shown that approximate graph schemas were more compact than the conventional accurate graph schemas and promising in query evaluation that involved regular path expressions.
All Author(s) ListWang QY, Yu JX, Wong KF
Name of Conference7th International Conference on Extending Database Technology
Start Date of Conference27/03/2000
End Date of Conference31/03/2000
Place of ConferenceCONSTANCE
Journal nameLecture Notes in Artificial Intelligence
Year2000
Month1
Day1
Volume Number1777
PublisherSPRINGER-VERLAG BERLIN
Pages302 - 316
ISBN3-540-67227-3
ISSN0302-9743
LanguagesEnglish-United Kingdom
Web of Science Subject CategoriesComputer Science; Computer Science, Information Systems; Computer Science, Theory & Methods

Last updated on 2021-13-01 at 23:42