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A Framework for Integrating XML Transformations

Authors: Ce Dong; James Bailey;

A Framework for Integrating XML Transformations

Abstract

XML is the de facto standard for representing and exchanging data on the World Wide Web and XSLT is a primary language for XML transformation. Integration of XML data is an increasingly important problem and many methods have been developed. In this paper, we study the related and more difficult problem of how to integrate XSLT programs. Program integration can be particularly important for server-side XSLT applications, where it is necessary to generate a global XSLT program, that is a combination of some initial XSLT programs and which is required to operate over a newly integrated XML database. This global program should inherit as much functionality from the initial XSLT programs as possible, since designing a brand new global XSLT program from scratch could be expensive, slow and error prone, especially when the initial XSLT programs are large or/and complicated. However, it is a challenging task to develop methods to support XSLT integration. Difficulties such as template identification, unmapped template processing and template equivalence all need to be resolved. In this paper, we propose a framework for semi-automatic integration of XSLT programs. Our method makes use of static analysis techniques for XSLT and consists of four key steps: i) Pattern Specialization, ii) Template Translation, iii) Lost Template Processing and iv) Program Integration. We are not aware of any previous work that deals with integrating XML transformations.

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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!
1
Average
Average
Average
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