The World Wide Web (Web) has evolved from a collection of static pages that need reloading every time the content changes, into dynamic pages where parts of the page updates independently, without reloading it. As such, users are required to work with dynamic pages with components that react to events either from human interaction or machine automation. Often elderly and visually impaired users are the most disadvantaged when dealing with this form of interaction. Operating widgets require the user to have the conceptual design knowledge of the widget to complete the task. Users must have prior experience with the widget or have to learn to operate it independently, because often no user documentation is available. An automated Widget Prediction Framework (WPF) is proposed to address the issues discussed. It is a pre-emptive approach that predicts different types of widget and their locations in the page. Widgets with similar characteristics and functionalities are categorised based on a definition provided by widget design pattern libraries. Some design patterns are more loosely defined than others, and this causes confusion and ambiguity when identifying them. A formal method to model widgets based on a Widget Ontology was developed. The paradigm of the ontology provides a framework for developers to communicate their ideas, while reducing ambiguity between different types of widget. A Widget Prediction System (WPS) was developed using the concepts of the WPF. To select the types of widget for WPS evaluation, a widget popularity investigation was conducted. Seven of the most popular widgets from the investigation, done across fifty Websites, were selected. To demonstrate how WPF can be applied to predict widgets, fifty websites were used to evaluate the feasibility of the approach using WPS. On average, WPS achieved 61.98% prediction accuracy with two of the widgets > 84% accuracy. These results demonstrated the feasibility of the framework as the backend for tools that support elderly or visually impaired users.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:588142 |
| Date | January 2013 |
| Creators | Chen, Alex Qiang |
| Contributors | Harper, Simon; Brown, Andrew |
| Publisher | University of Manchester |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | https://www.research.manchester.ac.uk/portal/en/theses/automatic-web-widgets-prediction-for-web-20-access-technologies(24e7a9d5-7e03-4fe3-a65e-4f4930a4f5c1).html |
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