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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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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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