توضیحات
Abstract
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
Publisher:ELSEVIER
Year:2015
By: Gema Bello – Orgaz , Jason J. Jung , David Camacho
File Information:Englesh Language/15 Page/Size:486 K
Download:click
سال:2015
کاری از:Gema Bello – Orgaz , Jason J. Jung , David Camacho
اطلاعات فایل:زبان انگلیسی/15 صفحه/حجم:486K
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