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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Abstract

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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Background

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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Editorial Advisory Board
Table of Contents
Foreword
Barry Smyth
Preface
Constantinos Mourlas, Panagiotis Germanakos
Acknowledgment
Constantinos Mourlas, Panagiotis Germanakos
Chapter 1
Nikos Tsianos, Panagiotis Germanakos, Zacharias Lekkas, Constantinos Mourlas
The plethora of information and services as well as the complicated nature of most Web structures intensify the navigational difficulties that arise... Sample PDF
Assessment of Human Factors in Adaptive Hypermedia Environments
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Chapter 2
Barry Smyth
Everyday hundreds of millions of users turn to the World-Wide Web as their primary source of information during their educational, business and... Sample PDF
Case Studies in Adaptive Information Access: Navigation, Search, and Recommendation
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Chapter 3
Sherry Y. Chen
Web-based instruction is prevalent in educational settings. However, many issues still remain to be investigated. In particular, it is still open... Sample PDF
The Effects of Human Factors on the Use of Web-Based Instruction
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Chapter 4
Gulden Uchyigit
Coping with today’s unprecedented information overload problem necessitates the deployment of personalization services. Typical personalization... Sample PDF
The Next Generation of Personalization Techniques
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Chapter 5
Nancy Alonistioti
This chapter introduces context-driven personalisation of service provision based on a middleware architectural approach. It describes the emerging... Sample PDF
Advanced Middleware Architectural Aspects for Personalised Leading-Edge Services
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Chapter 6
Syed Sibte Raza Abidi
This chapter introduces intelligent information personalization as an approach to personalize the webbased information retrieval experiences based... Sample PDF
Intelligent Information Personalization: From Issues to Strategies
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Chapter 7
Babis Magoutas
This chapter introduces a semantically adaptive interface as a means of measuring the quality of egovernment portals, based on user feedback. The... Sample PDF
A Semantically Adaptive Interface for Measuring Portal Quality in E-Government
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Chapter 8
Fabio Grandi, Federica Mandreoli, Riccardo Martoglia, Enrico Ronchetti, Maria Rita Scalas
While the World Wide Web user is suffering form the disease caused by information overload, for which personalization is one of the treatments which... Sample PDF
Ontology-Based Personalization of E-Government Services
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Chapter 9
Maria Golemati, Costas Vassilakis, Akrivi Katifori, George Lepouras, Constantin Halatsis
Novel and intelligent visualization methods are being developed in order to accommodate user searching and browsing tasks, including new and... Sample PDF
Context and Adaptivity-Driven Visualization Method Selection
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Chapter 10
Honghua Dai
Web usage mining has been used effectively as an approach to automatic personalization and as a way to overcome deficiencies of traditional... Sample PDF
Integrating Semantic Knowledge with Web Usage Mining for Personalization
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Chapter 11
Constantinos Mourlas
One way to implement adaptive software is to allocate resources dynamically during run-time rather than statically at design time. Design of... Sample PDF
Adaptive Presentation and Scheduling of Media Streams on Parallel Storage Servers
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Chapter 12
Gheorghita Ghinea
This study investigated two dimensions of cognitive style, including Verbalizer/Imager and Field Dependent/ Field Independent and their influence on... Sample PDF
Impact of Cognitive Style on User Perception of Dynamic Video Content
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Chapter 13
Mathias Bauer, Alexander Kröner, Michael Schneider, Nathalie Basselin
Limitation of the human memory is a well-known issue that anybody has experienced. This chapter discusses typical components and processes involved... Sample PDF
Building Digital Memories for Augmented Cognition and Situated Support
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Chapter 14
Rafael Morales, Nicolas Van Labeke, Paul Brna, María Elena Chan
It is believed that, with the help of suitable technology, learners and systems can cooperate in building a sufficiently accurate learner model they... Sample PDF
Open Learner Modelling as the Keystone of the Next Generation of Adaptive Learning Environments
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Chapter 15
Klaus Jantke, Christoph Igel, Roberta Sturm
Humans need assistance in learning. This is particularly true when learning is supported by modern information and communication technologies. Most... Sample PDF
From E-Learning Tools to Assistants by Learner Modelling and Adaptive Behavior
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Chapter 16
Violeta Damjanovic, Milos Kravcik
The process of training and learning in Web-based and ubiquitous environments brings a new sense of adaptation. With the development of more... Sample PDF
Using Emotional Intelligence in Personalized Adaptation
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Chapter 17
Yang Wang
This chapter presents a first-of-its-kind survey that systematically analyzes existing privacy-enhanced personalization (PEP) solutions and their... Sample PDF
Technical Solutions for Privacy- Enhanced Personalization
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About the Contributors