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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]
Copyright of Soft Computing - A Fusion of Foundations, Methodologies & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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  Data: An influence assessment method based on co-occurrence for topologically reduced big data sets.
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  Data: <searchLink fieldCode="AR" term="%22Trovati%2C+Marcello%22">Trovati, Marcello</searchLink><relatesTo>1</relatesTo><i> M.Trovati@derby.ac.uk</i><br /><searchLink fieldCode="AR" term="%22Bessis%2C+Nik%22">Bessis, Nik</searchLink><relatesTo>1</relatesTo><i> n.bessis@derby.ac.uk</i>
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  Data: <searchLink fieldCode="DE" term="%22Data+mining+--+Social+aspects%22">Data mining -- Social aspects</searchLink><br /><searchLink fieldCode="DE" term="%22Data+analytics%22">Data analytics</searchLink><br /><searchLink fieldCode="DE" term="%22Big+data%22">Big data</searchLink><br /><searchLink fieldCode="DE" term="%22Bayesian+analysis%22">Bayesian analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+network+management%22">Computer network management</searchLink><br /><searchLink fieldCode="DE" term="%22Management%22">Management</searchLink>
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  Data: 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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  Data: <i>Copyright of Soft Computing - A Fusion of Foundations, Methodologies & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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