Details

Autor(en) / Beteiligte

• Braun, Peter
• Cuzzocrea, Alfredo
• Jiang, Fan
• Leung, Carson Kai-Sang
• Pazdor, Adam G. M.

Titel

MapReduce-Based Complex Big Data Analytics over Uncertain and Imprecise Social Networks

Ist Teil von

• Big Data Analytics and Knowledge Discovery, p.130-145

Ort / Verlag

Cham: Springer International Publishing

Link zum Volltext

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• With advances in technology, high volumes of valuable but complex data can be easily collected and generated from various sources in the current era of big data. A prime source of these complex big data is the social network, in which users are often linked by some interdependencies such as friendships and follower-followee relationships. These interdependencies can be uncertain and imprecise. Moreover, as the social network keeps growing, there are situations in which individual users or businesses want to find those popular (i.e., frequently followed) groups of users so that they can follow the same groups. In this paper, we present a complex big data analytic solution that uses the MapReduce model to mine uncertain and imprecise social networks for discovering groups of potentially popular users. Evaluation results show the efficiency and practicality of our solution in conducting complex big data analytics over uncertain and imprecise social networks.