Knowledge discovery from social graph data
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Date
2016
Authors
Braun, Peter
Cuzzocrea, Alfredo
Leung, Carson K.
Pazdor, Adam G.M.
Tran, Kimberly
Journal Title
Journal ISSN
Volume Title
Publisher
Elsevier
Abstract
High volumes of a wide variety of valuable data can be easily collected and generated from a broad range of data sources of different veracities at a high velocity. In the current era of big data, many traditional data management and analytic approaches may not be suitable for handling the big data due to their well-known 5V's characteristics. Over the past few years, several systems and applications have developed to use cluster, cloud or grid computing to manage and analyze big data so as to support data science (e.g., knowledge discovery and data mining). In this paper, we present a knowledge-based system for social network analysis so as to support big data mining of interesting patterns from big social networks that are represented as graphs.
Description
P. Braun, A. Cuzzocrea, C.K. Leung, A.G.M. Pazdor, K. Tran. Knowledge discovery from social graph data. Procedia Computer Science, 96 (2016), pp. 682-691. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Keywords
knowledge discovery and data mining, big data, big data management, big data analysis, graph data, data and knowledge representation, knowledge technologies
Citation
P. Braun, A. Cuzzocrea, C.K. Leung, A.G.M. Pazdor, K. Tran. Knowledge discovery from social graph data. Procedia Computer Science, 96 (2016), pp. 682-691