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.



Titolo: Knowledge Discovery from Social Graph Data
Autori: 
CUZZOCREA, Alfredo Massimiliano
mostra contributor esterni 
Data di pubblicazione: 2016
Stato di pubblicazione: Pubblicato
Rivista: 
PROCEDIA COMPUTER SCIENCE  
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.
Handle: http://hdl.handle.net/11368/2897947
Digital Object Identifier (DOI): http://dx.doi.org/10.1016/j.procs.2016.08.250
URL: http://www.sciencedirect.com/science/article/pii/S1877050916320610?via%3Dihub
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