Context and Adaptivity-Driven Visualization Method Selection

Context and Adaptivity-Driven Visualization Method Selection

Maria Golemati (University of Athens, Greece), Costas Vassilakis (University of Peloponnese, Greece), Akrivi Katifori (University of Athens, Greece), George Lepouras (University of Peloponnese, Greece) and Constantin Halatsis (University of Athens, Greece)
DOI: 10.4018/978-1-60566-032-5.ch009
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Novel and intelligent visualization methods are being developed in order to accommodate user searching and browsing tasks, including new and advanced functionalities. Besides, research in the field of user modeling is progressing in order to personalize these visualization systems, according to its users’ individual profiles. However, employing a single visualization system, may not suit best any information seeking activity. In this paper we present a visualization environment, which is based on a visualization library, i.e. is a set of visualization methods, from which the most appropriate one is selected for presenting information to the user. This selection is performed combining information extracted from the context of the user, the system configuration and the data collection. A set of rules inputs such information and assigns a score to all candidate visualization methods. The presented environment additionally monitors user behavior and preferences to adapt the visualization method selection criteria.
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The problem of context management constitutes a new approach to the design of context-aware systems. (Zimmermann A., Specht M. & Lorenz A., 2005) refers to this problem combining personalization and contextualization. It defines that an adaptive system (contextualized and personalized or both) follows an adaptation strategy (e.g. pacing or leading) to achieve an adaptation goal (e.g. intuitive information access or easy use of a service). To achieve an adaptation goal, it considers relevant information about the user and the context and adapts relevant system components on the basis of this information”.

(Domik G. O. & Gutkauf B, 1994) claims that a visualization system needs to adapt to desires, abilities and disabilities of the user, interpretation aim, resources (hardware, software) available, and the form and content of the data to be visualized. It distinguishes four different models: user model, problem domain/task model, resource model and data model and gives the design of computer tests and games to test user abilities (color perception, colour memory, colour ranking, mental rotation and motor coordination). (Fischer G., Lemke A., Mastaglio T., & Morch A., 1991) suggests the following three kinds of user modeling:

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