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

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Title: An influence assessment method based on co-occurrence for topologically reduced big data sets.
Authors: Trovati, Marcello1 M.Trovati@derby.ac.uk, Bessis, Nik1 n.bessis@derby.ac.uk
Source: Soft Computing - A Fusion of Foundations, Methodologies & Applications. May2016, Vol. 20 Issue 5, p2021-2030. 10p.
Subjects: Data mining -- Social aspects, Data analytics, Big data, Bayesian analysis, Computer network management, Management
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. [ABSTRACT FROM AUTHOR]
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Database: Engineering Source
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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. [ABSTRACT FROM AUTHOR]
ISSN:14327643
DOI:10.1007/s00500-015-1621-9