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Adaptive Layouts and Nesting Templates for Hypermedia Composite Templates

Published:27 October 2015Publication History

ABSTRACT

Arranging interface components in a layout model, like a grid, is frequently supported in programming languages. However, in multimedia authoring declarative languages, those facilities are not provided and authors should declare presentation characteristics for each media item separately. Moreover, in hypermedia document template authoring languages, where generic media components can be defined, layout models would be very helpful for specifying presentation characteristics of media items. This paper proposes the use of Adaptive Layouts for hypermedia document templates. Adaptive Layouts allow the definition of generic layout models, such as grid or flow, to automatically place media items on the presentation device screen. This work presents the XTemplate 4.0 authoring language for specifying hypermedia composite templates including the adaptive layout facility. Another contribution of this work is related to template nesting. When defining new document templates, reusing previously-defined ones can be very helpful. As a hypermedia composite template specifies semantics for a document composite node, when nested compositions are supported in the document authoring model, nested templates can facilitate template definition and reuse. This work also includes the facility of nesting templates in XTemplate 4.0, specifying a well-defined interface for nesting templates, which satisfies the compositionality property.

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            cover image ACM Other conferences
            WebMedia '15: Proceedings of the 21st Brazilian Symposium on Multimedia and the Web
            October 2015
            266 pages
            ISBN:9781450339599
            DOI:10.1145/2820426

            Copyright © 2015 ACM

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            New York, NY, United States

            Publication History

            • Published: 27 October 2015

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            WebMedia '15 Paper Acceptance Rate21of61submissions,34%Overall Acceptance Rate270of873submissions,31%

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