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Using clustering to support the migration from static to dynamic web pages

Authors: RICCA, FILIPPO; P. Tonella;

Using clustering to support the migration from static to dynamic web pages

Abstract

Web sites of the first generation consist typically of a set of purely static Web pages. Content and presentation are not separated, and a same page structure is replicated every time a similar organization of the information is devised. Such a practice poses several problems to the evolution of these sites. It is not easy to update the content, and each time the HTML structure is modified, the same changes have to be propagated to all replications. In this paper, an approach is proposed for the identification of the Web pages that are more amenable to be migrated into a dynamic version, in that they share a similar structure, filled in with a content organized according to a common scheme. Clustering is used for this purpose: a common template is extracted from the pages in the same cluster and the variable information of the pages matching the template is migrated to a database. A server side program extracts the requested information from the data base, and generates dynamically the HTML pages to be displayed in the browser.

Country
Italy
Related Organizations
Keywords

Dynamic Web-pages HTML pages Page structures Server-side programs

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    Average
    influence
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    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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Powered by OpenAIRE graph
Found an issue? Give us feedback
selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
47
Average
Top 10%
Top 10%
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