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Please use this identifier to cite or link to this item: http://hdl.handle.net/123456789/2975

Title: Modeling Pervasive Context-aware Museum Guide Service (PCMGS)
Authors: Bethelhem, Teka
Advisors: Dejene Ejigu (PhD)
Keywords: Pervasive Computing
Context-awareness,
Museum Guide Service
Preference Discovery
Copyright: Dec-2009
Date Added: 11-May-2012
Publisher: AAU
Abstract: Enhancement of hardware technology with advancement in application of pervasive computing shapes mobile users to seek devices on their disposal which may accomplish their day-to-day activities. Pervasive environments are dedicated to serve their users through embedded and/or independent physical devices. These environments have knowledgebase to learn from. By incorporating the incoming user and environment context with the stored knowledgebase, they prepare services on behalf of their users. The number of users of such environments is different with respect to the area of application where the pervasive system gets deployed. If there is more than one user in the environment, interest of each as of their personal profile should be considered. Among the promising application domains of pervasive computing, tourism is the one that is functional with multi-users (large number of tourists to be served at a time). Having little or no information about the area they are visiting, tourists are potential beneficiaries of pervasive guide systems. To this end, this paper reports our research effort that models a pervasive context-aware museum guide service along with its implementation detail. Our model discovers visitor preferences or prepares list of selected heritages from the museum customized as to visitor’s profile, analyzes each preference or proposed heritage to assure if it is liked by the visitor or not, and then monitors associating guide service with the analyzed heritages. The research theme of our model grounds on visitor satisfaction and design of expressive museum environment which are directly related with quality of guide service.
URI: http://hdl.handle.net/123456789/2975
Appears in:Thesis - Computer Science

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