Supporting Social Information Discovery from Big Uncertain Social Key-Value Data via Graph-Like Metaphors

In the current era of big data, huge volumes of a wide variety of valuable data of different veracity (e.g., uncertain data) can be easily collected and generated from a broad range of data sources (e.g., social networking sites) at a high velocity in various real-life applications. Many traditional data management and analytic approaches may not be suitable for handling the big data due to their well-known 5V’s characteristics. In this paper, we present a cognitive-based system for social network analysis. Our system supports information discovery of interesting social patterns from big uncertain social networks—which are represented in the form of key-value pairs—capturing the perceived likelihood of the linkages among the social entities in the network.

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