The extraction of meaningful, accurate, and relevant information is at the core of Big Data research. Furthermore, the ability to obtain an insight is essential in any decision-making process, even though the diverse and complex nature of big data sets raises a multitude of challenges. In this paper, we propose a novel method to address the automated assessment of influence among concepts in big data sets. This is carried out by investigating their mutual co occurrence, which is determined via topologically reducing the corresponding network. The main motivation is to provide a toolbox to classify and analyse influence properties, which can be used to investigate their dynamical and statistical behavior, potentially leading to a better understanding and prediction of the properties of the system(s) they model. An evaluation was carried out on two real-world data sets, which were analysed to test the capabilities of our system. The results show the potential of our approach, indicating both accuracy and efficiency.
|Journal||Soft Computing - A Fusion of Foundations, Methodologies and Applications|
|Early online date||24 Feb 2015|
|Publication status||E-pub ahead of print - 24 Feb 2015|