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Author Algergawy, Alsayed ♦ Mesiti, Marco ♦ Nayak, Richi ♦ Saake, Gunter
Source ACM Digital Library
Content type Text
Publisher Association for Computing Machinery (ACM)
File Format PDF
Copyright Year ©2011
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword XML data ♦ Clustering ♦ Documentation ♦ Schema matching ♦ Semantic similarity ♦ Structural similarity ♦ Tree similarity
Abstract In the last few years we have observed a proliferation of approaches for clustering XML documents and schemas based on their structure and content. The presence of such a huge amount of approaches is due to the different applications requiring the clustering of XML data. These applications need data in the form of similar contents, tags, paths, structures, and semantics. In this article, we first outline the application contexts in which clustering is useful, then we survey approaches so far proposed relying on the abstract representation of data (instances or schema), on the identified similarity measure, and on the clustering algorithm. In this presentation, we aim to draw a taxonomy in which the current approaches can be classified and compared. We aim at introducing an integrated view that is useful when comparing XML data clustering approaches, when developing a new clustering algorithm, and when implementing an XML clustering component. Finally, the article moves into the description of future trends and research issues that still need to be faced.
ISSN 03600300
Age Range 18 to 22 years ♦ above 22 year
Educational Use Research
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2011-10-01
Publisher Place New York
e-ISSN 15577341
Journal ACM Computing Surveys (CSUR)
Volume Number 43
Issue Number 4
Page Count 41
Starting Page 1
Ending Page 41


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Source: ACM Digital Library