An Embedded Tree Data Model for Web Content Adaptation

This thesis presents a novel data model named Content Container Tree (CCT) to enable dynamic web content adaptation for heterogeneous terminal devices. The rationale for the CCT data model is that a web page can be visually divided into several segments and each segment can be iteratively divided into several smaller sub-segments until certain granularity level has been reached; the hierarchy of the web page segments can be expressed with a tree structure; thus, by traversing this segmentation tree, the user can find and locate a particular segment of interest which is at the dimension comparable to the screen size of the terminal device. The CCT data model organizes the semantics of the hierarchical content segments with a tree structure and embeds this semantic tree into the content DOM tree to enable the user to navigate through the content segments at different levels and retrieve the target content segment according to their semantics. Based on the CCT data model, the high level structural adaptation can be explicitly separated from the low level presentational adaptation, resulting in a highly modular and extensible framework for web content adaptation which is proposed in this thesis too. The CCT web content adaptation approach is distinguished from other approaches in the following aspects: first, the CCT approach is data oriented rather than process oriented; second, the CCT approach supports multilevel granularity of the contents; finally, CCT approach is dynamic; result pages can be generated on the fly by request. As an empirical evaluation, a content adaptation application is built according to the CCT adaptation framework to show the feasibility and effectiveness of our approach.