What is it about?
Online social media data is growing exponentially. We introduce a novel model that segments the online social network and assign and associate with intelligent agents for analytics. Online social networks (OSN) are facing challenges since they have been extensively applied to different domains including online social media, e-commerce, biological complex networks, financial analysis, and so on. One of the crucial challenges for OSN lies in information overload and network congestion. The demands for efficient knowledge discovery and data mining methods in OSN have been rising in recent year, particularly for online social applications, such as Flickr, YouTube, Facebook, and LinkedIn. In this paper, a Belief-Desire-Intention (BDI) agentbased method has been developed to enhance the capability of mining online social networks. Current data mining techniques encounter difficulties of dealing with knowledge interpretation based on complex data sources. The proposed agent-based mining method overcomes network analysis difficulties, while enhancing the knowledge discovery capability through its autonomy and collective intelligence.
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Why is it important?
This research utilizes intelligent agents for online social data analytics. The model is noval since it first segments online social data sets and associated data with intelligent agents. An online social network can be segmented and assigned to agents for self-processing.
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This page is a summary of: Utilizing BDI Agents and a Topological Theory for Mining Online Social Networks, Fundamenta Informaticae, January 2013, IOS Press, DOI: 10.3233/fi-2013-922.
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