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Evaluation of Data Warehouse Design Methodologies in the Context of Big Data Read chapter Read first chapter

2017 | OriginalPaper | Chapter

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

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

Published in: Big Data Analytics and Knowledge Discovery

Publisher: 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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previous chapter An Efficient Map-Reduce Framework to Mine Periodic Frequent Patterns
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Metadata
Title
MapReduce-Based Complex Big Data Analytics over Uncertain and Imprecise Social Networks
Authors
Peter Braun
Alfredo Cuzzocrea
Fan Jiang
Carson Kai-Sang Leung
Adam G. M. Pazdor
Copyright Year
2017
Book
Big Data Analytics and Knowledge Discovery

Print ISBN: 978-3-319-64282-6

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

Copyright Year: 2017

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

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