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2017 | OriginalPaper | Buchkapitel

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

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

Erschienen in: Big Data Analytics and Knowledge Discovery

Verlag: Springer International Publishing

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Abstract

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.

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Vorheriges Kapitel An Efficient Map-Reduce Framework to Mine Periodic Frequent Patterns

Nächstes Kapitel A Case for Abstract Cost Models for Distributed Execution of Analytics Operators

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Titel: MapReduce-Based Complex Big Data Analytics over Uncertain and Imprecise Social Networks
verfasst von: Peter Braun
Alfredo Cuzzocrea
Fan Jiang
Carson Kai-Sang Leung
Adam G. M. Pazdor
Verlag: Springer International Publishing
Buch: Big Data Analytics and Knowledge Discovery
Print ISBN: 978-3-319-64282-6

Electronic ISBN: 978-3-319-64283-3

Copyright-Jahr: 2017
DOI: https://doi.org/10.1007/978-3-319-64283-3_10

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