Open Source Analytics Solutions for Maintenance

Erkki Jantunen, Jaime Campos, Pankaj Sharma, Mark McKay

    Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

    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.
    Original languageEnglish
    Title of host publicationProceedings of the 5th International Conference on Control, Decision and Information Technologies
    PublisherIEEE Institute of Electrical and Electronic Engineers
    Pages688-693
    Number of pages6
    ISBN (Electronic)978-1-5386-5065-3
    DOIs
    Publication statusPublished - 13 Apr 2018
    MoE publication typeA4 Article in a conference publication
    Event5th International Conference on Control, Decision and Information Technologies, CoDIT 2018 - The Grand Palace Hotel, Thessaloniki, Greece
    Duration: 10 Apr 201813 Apr 2018

    Conference

    Conference5th International Conference on Control, Decision and Information Technologies, CoDIT 2018
    Abbreviated titleCoDIT 2018
    CountryGreece
    CityThessaloniki
    Period10/04/1813/04/18

    Fingerprint

    Data mining
    Industry
    Decision making
    Big data

    Keywords

    • data mining
    • big data
    • open source
    • maintenance
    • CBM

    Cite this

    Jantunen, E., Campos, J., Sharma, P., & McKay, M. (2018). Open Source Analytics Solutions for Maintenance. In Proceedings of the 5th International Conference on Control, Decision and Information Technologies (pp. 688-693). IEEE Institute of Electrical and Electronic Engineers . https://doi.org/10.1109/CoDIT.2018.8394819
    Jantunen, Erkki ; Campos, Jaime ; Sharma, Pankaj ; McKay, Mark. / Open Source Analytics Solutions for Maintenance. Proceedings of the 5th International Conference on Control, Decision and Information Technologies. IEEE Institute of Electrical and Electronic Engineers , 2018. pp. 688-693
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    title = "Open Source Analytics Solutions for Maintenance",
    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.",
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    Jantunen, E, Campos, J, Sharma, P & McKay, M 2018, Open Source Analytics Solutions for Maintenance. in Proceedings of the 5th International Conference on Control, Decision and Information Technologies. IEEE Institute of Electrical and Electronic Engineers , pp. 688-693, 5th International Conference on Control, Decision and Information Technologies, CoDIT 2018, Thessaloniki, Greece, 10/04/18. https://doi.org/10.1109/CoDIT.2018.8394819

    Open Source Analytics Solutions for Maintenance. / Jantunen, Erkki; Campos, Jaime; Sharma, Pankaj; McKay, Mark.

    Proceedings of the 5th International Conference on Control, Decision and Information Technologies. IEEE Institute of Electrical and Electronic Engineers , 2018. p. 688-693.

    Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

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    AB - 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.

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    Jantunen E, Campos J, Sharma P, McKay M. Open Source Analytics Solutions for Maintenance. In Proceedings of the 5th International Conference on Control, Decision and Information Technologies. IEEE Institute of Electrical and Electronic Engineers . 2018. p. 688-693 https://doi.org/10.1109/CoDIT.2018.8394819