Knowledge Discovery from Social Graph Data

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• 作者：Peter Braun^a ; Alfredo Cuzzocrea^b ; Carson K. Leung^a ; ^{kleung@cs.umanitoba.ca" class="auth_mail" title="E-mail the corresponding author} ; Adam G.M. Pazdor^a ; Kimberly Tran^a
• 关键词：Knowledge discovery and data mining ; big data ; big data management ; big data analysis ; graph data ; data and knowledge representation ; knowledge technologies
• 刊名：Procedia Computer Science
• 出版年：2016
• 出版时间：2016
• 年：2016
• 卷：96
• 期：Complete
• 页码：682-691
• 全文大小：286 K

文摘

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.