Discovering associations in XML data

A. Meisels, M. Orlov, T. Maor

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

Knowledge inference from semi-structured data can utilize frequent sub structures, in addition to frequency of data items. In fact, the working assumption of the present study is that frequent sub-trees of XML data represent sets of tags (objects) that are meaningfully associated. A method for extracting frequent sub-trees from XML data is presented. It uses thresholds on frequencies of paths and on the multiplicity of paths in the data. The frequent sub-trees are extracted and counted in a procedure that has O(n2) complexity. The data content of the extracted sub-trees, in the form of attribute values, is cast in tabular form. This enables a search for associations in the extracted data. Thus, the complete procedure uses structure and content to extract association rules from semistructured data. A large industrial example is used to demonstrate the operation of the proposed method.

Original languageEnglish
Title of host publicationWISE 2002 - Proceedings of the 3rd International Conference on Web Information Systems Engineering Workshops
EditorsBo Huang, Tok Wang Ling, Ji-Rong Wen, S.K. Gupta, Wee Keong Ng, Mukesh Mohania
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages178-183
Number of pages6
ISBN (Electronic)0769518133, 9780769518138
DOIs
StatePublished - 2002
Externally publishedYes
Event3rd International Conference on Web Information Systems Engineering Workshops, WISE 2002 - Singapore, Singapore
Duration: 11 Dec 2002 → …

Publication series

NameWISE 2002 - Proceedings of the 3rd International Conference on Web Information Systems Engineering Workshops

Conference

Conference3rd International Conference on Web Information Systems Engineering Workshops, WISE 2002
Country/TerritorySingapore
CitySingapore
Period11/12/02 → …

Bibliographical note

Publisher Copyright:
© 2002 IEEE.

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