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Context-Aware Adaptation
Context-Aware Adaptation
Context-Aware Adaptation
Context-Aware Adaptation
Context-Aware Adaptation
Context-Aware Adaptation
Context-Aware Adaptation
Context-Aware Adaptation
Context-Aware Adaptation
Context-Aware Adaptation
Context-Aware Adaptation
Context-Aware Adaptation
Context-Aware Adaptation
Context-Aware Adaptation
Context-Aware Adaptation
Context-Aware Adaptation
Context-Aware Adaptation
Context-Aware Adaptation
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Context-Aware Adaptation

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  • 1. A Computational Frameworkfor Multi-dimensional Context-aware Adaptation Vivian Genaro Motti LILAB – Louvain Interaction Laboratory Université catholique de Louvain Belgium Pisa, June 13th , 2011
  • 2. Why adaptation?¤  Contexts of use vary ¤  Multiple devices, platforms ¤  Heterogeneous users ¤  Domains of application ¤  Many environments¤  Complex to implement specific versions of a system
  • 3. Agenda¤  Motivation¤  Problem¤  State of the art¤  Methodology ¤  Approach ¤  Validation and Evaluation¤  Next steps
  • 4. Motivation¤  Users interact from different contexts ¤  Taking the context information into account in a way to provide users a better interaction ¤  Regardless of environment, platform, profile and context of use
  • 5. Problem¤  Considering a pre-defined context of use (able-bodied user, stable environment, desktop PC) may difficult or prevent user interaction¤  Implementing multiple versions of the same application requires a significant effort¤  Challenge: consider the context to provide users adapted interfaces with high usability level
  • 6. State of the Art¤  Taxonomies¤  Modeling approaches (e.g. OWL for the context)¤  Machine Learning techniques¤  Architectures¤  Case studies¤  Limitations: ¤  The approaches are not unified and consistent ¤  They are limited (e.g. one dimension, or one platform at a time)
  • 7. Methodology¤  Gather information ¤  About context and adaptation with a Systematic Review¤  Implement adaptation algorithms ¤  Techniques and inference ¤  Map models of different abstraction levels¤  Case studies for evaluation and validation
  • 8. Approach Multi-dimensional Context-aware Adaptation Framework Gathering Implementation ValidationSystematic Listing Machine Review Techniques Models Learning Case Studies Iterative Evaluation
  • 9. Adaptation Technique: Re-moldingReferences [Coutaz, 2006]; [Serna et al., 2010]Description Re-molding consists in the reconfiguration the UI according to the target context: elements can be re-located, re-sized, added and supplied. Pagination and scrolling may be used.Rationale Given a UI and a target context, the elements are re- arranged for the new context to assure usabilityExample When the user changes the platform (e.g. from a Desktop PC to a Smartphone)Context According to the platform, device, screen dimensionsAdvantages The usability level will be improvedDisadvantages It is necessary to know before hand the best location for the elements, some of them may be suppressed Source: http://www.alistapart.com/articles/switchymclayout
  • 10. Modeling¤  UML and MOF diagrams
  • 11. Machine LearningBayesian Networks Decision Trees Markov Models useE.g.: To adapt the To perform previous informationnavigation by adaptation rules (e.g. history of userpredicting and according to the interaction) to predictsuggesting the next context of the user, the next stepstep to the user adapts some system aspects
  • 12. Model-driven ApproachTask and Abstract UI Concrete UI Final UI Domain News Serenoa [Serenoa] Serenoa is aimed Lorem ipsum Title lorem ipsum at developing a novel, open Display Text lorem ipsum platform for enabling the Content lorem Image ipsum creation of context-sensitive SFE.Text Images
  • 13. Evaluation¤  Evaluation plan ¤  Combined techniques ¤  Varied aspects ¤  Multiple scenarios ¤  Iterative ¤  Different fidelity levels ¤  User-centered design
  • 14. Validation¤  Case studies ¤  Different scenarios ¤  To define the feasibility
  • 15. Next Steps¤  Refine the techniques gathered¤  Define more complex adaptation ¤  Rules, methods, techniques¤  Perform evaluation¤  Validate
  • 16. Acknowledgments
  • 17. Bibliography¤  [Alpaydin] Ethem Alpaydin (2004) Introduction to Machine Learning. The MIT Press, October 2004, ISBN 0-262-01211-1¤  [Brusilovsky] Brusilovsky, P. (1996) Methods and Techniques of Adaptive Hypermedia.  In Proceedings of User Model. User-Adapt. Interact.. 1996, 87-129.¤  [Coutaz] Joelle Coutaz. 2006. Meta-user interfaces for ambient spaces. In Proceedings of the 5th international conference on Task models and diagrams for users interface design (TAMODIA06), Karin Coninx, Kris Luyten, and Kevin A. Schneider (Eds.). Springer-Verlag, Berlin, Heidelberg, 1-15.¤  [Dey] Anind K. Dey. (2001). Understanding and Using Context. Personal Ubiquitous Comput. 5, 1 (January 2001), 4-7. DOI=10.1007/s007790170019 http://dx.doi.org/ 10.1007/s007790170019¤  [Dessart] Dessart C. et. al. (2011) Showing User Interface Adaptivity by Animated Transitions. EICS’11 (to appear)¤  [Serna et al., 2010] Audrey Serna, Gaëlle Calvary, Dominique L. Scapin. How assessing plasticity design choices can improve UI quality: a case study. In Proceedings of EICS2010. pp.29~34
  • 18. Questions?

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