Abstract
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.
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Trovati, M., Bessis, N. An influence assessment method based on co-occurrence for topologically reduced big data sets. Soft Comput 20, 2021–2030 (2016). https://doi.org/10.1007/s00500-015-1621-9
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DOI: https://doi.org/10.1007/s00500-015-1621-9