Abstract
Due to a large amount of resources (i.e., information and knowledge) available on world wide web, it has been more difficult for users to effectively find relevant web resources. Most of the current web browsing methods and systems have been investigated to apply adaptive approaches which can extract personal contexts (e.g., interests and preferences) of the users. In this paper, we propose a contextual mashup-based collaborative browsing (co-browsing) platform, called ContextGrid, for providing online users with various knowledge sharing services. Particularly, the proposed mashup scheme can integrate heterogeneous pieces of information collected by various Open APIs, and assist the users to decide which partners should be selected for mutual collaborations. In order to evaluate the proposed mashup-based method, we have implemented a co-browsing platform which can exchange bookmarks, and measured whether the contextual mashup scheme makes a meaningful influence on improving the performance of the co-browsing process with multiple users.
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Notes
In terms of temporal and spatial characteristics, each co-browsing system can be either synchronous or asynchronous, and either local or remote.
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Acknowledgements
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MEST) (No. 2011-0017156).
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Jung, J.J. ContextGrid: A contextual mashup-based collaborative browsing system. Inf Syst Front 14, 953–961 (2012). https://doi.org/10.1007/s10796-011-9315-z
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DOI: https://doi.org/10.1007/s10796-011-9315-z