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XTRACT: learning document type descriptors from XML document collections

Garofalakis Minos, Gionis, Aristides P, Rastogi Rajeev , Seshadri Swamigal, Shim Kyuseok

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URIhttp://purl.tuc.gr/dl/dias/DC02EDEC-9C89-40AD-886A-434E6CDF2770-
Identifierhttp://link.springer.com/article/10.1023%2FA%3A1021560618289-
Identifierhttps://doi.org/10.1023/A:1021560618289-
Languageen-
Extent34 pagesen
TitleXTRACT: learning document type descriptors from XML document collectionsen
CreatorGarofalakis Minosen
CreatorΓαροφαλακης Μινωςel
CreatorGionis, Aristides Pen
CreatorRastogi Rajeev en
CreatorSeshadri Swamigalen
CreatorShim Kyuseoken
PublisherKluweren
Content SummaryXML is rapidly emerging as the new standard for data representation and exchange on the Web. Unlike HTML, tags in XML documents describe the semantics of the data and not how it is to be displayed. In addition, an XML document can be accompanied by a Document Type Descriptor (DTD) which plays the role of a schema for an XML data collection. DTDs contain valuable information on the structure of documents and thus have a crucial role in the efficient storage of XML data, as well as the effective formulation and optimization of XML queries. Despite their importance, however, DTDs are not mandatory, and it is frequently possible that documents in XML databases will not have accompanying DTDs. In this paper, we propose XTRACT, a novel system for inferring a DTD schema for a database of XML documents. Since the DTD syntax incorporates the full expressive power of regular expressions, naive approaches typically fail to produce concise and intuitive DTDs. Instead, the XTRACT inference algorithms employ a sequence of sophisticated steps that involve: (1) finding patterns in the input sequences and replacing them with regular expressions to generate “general” candidate DTDs, (2) factoring candidate DTDs using adaptations of algorithms from the logic optimization literature, and (3) applying the Minimum Description Length (MDL) principle to find the best DTD among the candidates. The results of our experiments with real-life and synthetic DTDs demonstrate the effectiveness of XTRACT's approach in inferring concise and semantically meaningful DTD schemas for XML databases.en
Type of ItemPeer-Reviewed Journal Publicationen
Type of ItemΔημοσίευση σε Περιοδικό με Κριτέςel
Licensehttp://creativecommons.org/licenses/by/4.0/en
Date of Item2015-10-29-
Date of Publication2003-
SubjectXMLen
SubjectExtensible Markup Languageen
SubjectDTDen
SubjectDocument Type Descriptoren
SubjectRegular expressionsen
SubjectMDL principleen
Bibliographic CitationM. Garofalakis, A. Gionis, R. Rastogi, S. Seshadri and K. Shim, "XTRACT: learning document type descriptors from XML document collections", Data Min. Knowl. Disc., vol. 7, no. 1, pp. 23-56, Jan. 2002. doi:10.1023/A:1021560618289en

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