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• W2810222022 abstract "The current paper reviews existent data mining and big data analytics open source solutions. In the area of industrial maintenance engineering, the algorithms, which are part of these solutions, have started to be studied and introduced into the domain. In addition, the interest in big data and analytics have increased in several areas because of the increased amount of data produced as well as a remarkable speed attained and its variation, i.e. the so-called 3 V's (Volume, Velocity, and Variety). The companies and organizations have seen the need to optimize their decision-making processes with the support of data mining and big data analytics. The development of this kind of solutions might be a long process and for some companies something that is not within their reach for many reasons. It is, therefore, important to understand the characteristics of the open source solutions. Consequently, the authors use a framework to organize their findings. Thus, the framework used is called the knowledge discovery in databases (KDD) process for extracting useful knowledge from volumes of data. The authors suggest a modified KDD framework to be able to understand if the respective data mining/big data solutions are adequate and suitable to use in the domain of industrial maintenance engineering." @default.
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• W2810222022 date "2018-04-01" @default.
• W2810222022 modified "2023-09-27" @default.
• W2810222022 title "Open Source Analytics Solutions for Maintenance" @default.
• W2810222022 cites W103914429 @default.
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• W2810222022 doi "https://doi.org/10.1109/codit.2018.8394819" @default.
• W2810222022 hasPublicationYear "2018" @default.
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