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

  • Calvin S. H. Hoi
  • Carson K. Leung
  • Kimberly Tran
  • Alfredo Cuzzocrea
  • Mario Bochicchio
  • Marco Simonetti
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10971)


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.


Cognitive computing Data mining Knowledge discovery Big data Social network Uncertain data Key-value store 



This project is partially supported by Natural Sciences and Engineering Research Council of Canada (NSERC) and the University of Manitoba.


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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.University of ManitobaManitobaCanada
  2. 2.University of TriesteTriesteItaly
  3. 3.ICAR-CNRRendeItaly
  4. 4.University of SalentoLecceItaly

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