An Entropy-Based Characterization of the Heterogeneity of XML Collections

Sanz, Ismael; Mesiti, Marco; Guerrini, Giovanna; Berlanga, Rafael

doi:10.1109/dexa.2008.55

Cited by 3 publications

(1 citation statement)

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“…In this strategy, therefore, the data characteristics suggest the measure to apply for clustering. Sanz et al [2008] propose the use of entropy-based measures to evaluate the level of heterogeneity of different aspects of documents. This information can be used to choose the more adequate indexing structure, and to find out whether it is better to use an exact or approximate retrieval approach.…”

Section: Summary and Future Directionsmentioning

confidence: 99%

XML data clustering

Algergawy¹,

Mesiti

Nayak

et al. 2011

ACM Comput. Surv.

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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.

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Section: Summary and Future Directionsmentioning

confidence: 99%