Journal
INFORMATION FUSION
Volume 28, Issue -, Pages 45-59Publisher
ELSEVIER
DOI: 10.1016/j.inffus.2015.08.005
Keywords
Big data; Data mining; Social media; Social networks; Social-based frameworks and applications
Funding
- Comunidad Autonoma de Madrid [CIBERDINE S2013/ICE-3095]
- Spanish Ministry of Science and Education [TIN2014-56494-C4-4-P]
- Savier Open Innovation Project (Airbus Defence Space) [FUAM-076915]
- National Research Foundation of Korea (NRF) - Korea government (MSIP) [NRF-2013K2A1A2055213, NRF-2014R1A 2A2A05007154]
- National Research Foundation of Korea [2014R1A2A2A05007154] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
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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.
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