Abstract
It is important to be aware of user contexts for supporting efficient collaborations among them. The goal of this paper is to present a social collaboration platform where we can understand i) how the user contexts are dynamically changing over time, and ii) how the user contexts are mixed with multiple sub-contexts together. Thereby, we have implemented TweetPulse, which is a a Twitter-based tool for context monitoring and propagation system in a given social network. TweetPulse can match contexts of the users (and integrate them) to find the most relevant users. Eventually, collaboration among users are contextually synchronized. by dynamically organizing a number of communities. A set of users in each community come together to share skills or core competencies and resources at the moment. We have shown the experimental results collected from a collaborative information searching system in terms of i) setting thresholds, ii) searching performance, and iii) scalability testing.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Berger, A.L., Pietra, S.D., Pietra, V.J.D.: A maximum entropy approach to natural language processing. Computational Linguistics 22(1), 39–71 (1996)
Edwards, W.K.: Putting computing in context: An infrastructure to support extensible context-enhanced collaborative applications. ACM Transactions on Computer-Human Interaction 12(4), 446–474 (2005), doi: http://doi.acm.org/10.1145/1121112.1121117
Gross, T., Prinz, W.: Modelling shared contexts in cooperative environments: Concept, implementation, and evaluation. Computer Supported Cooperative Work 13(3), 283–303 (2004)
Jung, J.J.: Collaborative web browsing based on semantic extraction of user interests with bookmarks. Journal of Universal Computer Science 11(2), 213–228 (2005)
Jung, J.J.: Visualizing recommendation flow on social networks. Journal of Universal Computer Science 11(11), 1780–1791 (2005)
Jung, J.J.: Ontological framework based on contextual mediation for collaborative information retrieval. Information Retrieval 10(1), 85–109 (2007), doi:10.1007/s10791-006-9013-5
Jung, J.J.: Ontology-based context synchronization for ad-hoc social collaborations. Knowledge-Based Systems 21(7), 573–580 (2008)
Jung, J.J.: Boosting social collaborations based on contextual synchronization: An empirical study. Expert Systems with Applications 38(5), 4809–4815 (2011)
Nunes, V.T., Santoro, F.M., Borges, M.R.: A context-based model for knowledge management embodied in work processes. Information Sciences 179(15), 2538–2554 (2009)
Schmidt, A.: Management of Dynamic and Imperfect User Context Information. In: Meersman, R., Tari, Z., Corsaro, A. (eds.) OTM-WS 2004. LNCS, vol. 3292, pp. 779–786. Springer, Heidelberg (2004)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Jung, J.J. (2013). Contextual Synchronization for Efficient Social Collaborations: A Case Study on TweetPulse. In: Fortino, G., Badica, C., Malgeri, M., Unland, R. (eds) Intelligent Distributed Computing VI. Studies in Computational Intelligence, vol 446. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32524-3_22
Download citation
DOI: https://doi.org/10.1007/978-3-642-32524-3_22
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-32523-6
Online ISBN: 978-3-642-32524-3
eBook Packages: EngineeringEngineering (R0)