Social big data: Recent achievements and new challenges
Social big data: Recent achievements and new challenges
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DOI：
10.1016/j.inffus.2015.08.005
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发表时间：
2016-03-01
影响因子：
18.6
通讯作者：
Camacho, David
中科院分区：
文献类型：
--
作者：
Bello-Orgaz, Gema;Jung, Jason J.;Camacho, David
大数据已经成为数据挖掘、机器学习、计算智能、信息融合、语义Web和社交网络等大量研究领域的重要问题。不同大数据框架的兴起，如Apache Hadoop和最近的Spark，基于MapReduce范式进行大规模数据处理，允许在不同领域有效利用数据挖掘方法和机器学习算法。许多库，如Mahout和SparkMLib，已经被设计用于开发基于机器学习算法的新的高效应用程序。大数据技术和传统机器学习算法的结合在社交媒体和社交网络等其他领域产生了新的有趣的挑战。这些新挑战主要集中在数据处理、数据存储、数据表示以及如何将数据用于模式挖掘、分析用户行为、可视化和跟踪数据等问题上。在本文中，我们提出了一个新的方法，旨在允许有效的数据挖掘和信息融合的社交媒体和新的应用程序和框架，目前出现的“保护伞”下的社交网络，社交媒体和大数据的范例的修订。(C)2015 Elsevier B.V.版权所有。
Big data has become an important issue for a large number of research areas such as data mining, machine learning, computational intelligence, information fusion, the semantic Web, and social networks. The rise of different big data frameworks such as Apache Hadoop and, more recently, Spark, for massive data processing based on the MapReduce paradigm has allowed for the efficient utilisation of data mining methods and machine learning algorithms in different domains. A number of libraries such as Mahout and SparkMLib have been designed to develop new efficient applications based on machine learning algorithms. The combination of big data technologies and traditional machine learning algorithms has generated new and interesting challenges in other areas as social media and social networks. These new challenges are focused mainly on problems such as data processing, data storage, data representation, and how data can be used for pattern mining, analysing user behaviours, and visualizing and tracking data, among others. In this paper, we present a revision of the new methodologies that is designed to allow for efficient data mining and information fusion from social media and of the new applications and frameworks that are currently appearing under the "umbrella" of the social networks, social media and big data paradigms. (C) 2015 Elsevier B.V. All rights reserved.