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

    1 Citation (Scopus)
    71 Downloads (Pure)


    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
    ISBN (Electronic)978-1-5386-5065-3
    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


    Conference5th International Conference on Control, Decision and Information Technologies, CoDIT 2018
    Abbreviated titleCoDIT 2018


    The research has been conducted as a part of MANTIS Cyber Physical System based Proactive Collaborative Maintenance project. The project has received funding from the Electronic Component Systems for European Leadership Joint Undertaking under grant agreement No 662189. This Joint Undertaking receives support from the European Union’s Horizon 2020 research and the national funding organisation Finnish Funding Agency for Innovation Tekes.


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


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