An influence assessment method based on co-occurrence for topologically reduced big data sets

作者：Trovati Marcello; Bessis Nik^*

来源：Soft Computing, 2016, 20(5): 2021-2030.

DOI：10.1007/s00500-015-1621-9

• Knowledge discovery
• Large-scale networks
• Information extraction
• Data analytics

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 behaviour, 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.

• 出版日期2016-5