Open Source Analytics Solutions for Maintenance

Erkki Jantunen; Jaime Campos; Pankaj Sharma; Mark McKay

doi:10.1109/CoDIT.2018.8394819

Open Source Analytics Solutions for Maintenance

Erkki Jantunen, Jaime Campos, Pankaj Sharma, Mark McKay

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

2 Citations (Scopus)

100 Downloads (Pure)

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 language	English
Title of host publication	Proceedings of the 5th International Conference on Control, Decision and Information Technologies
Publisher	IEEE Institute of Electrical and Electronic Engineers
Pages	688-693
ISBN (Electronic)	978-1-5386-5065-3
DOIs	https://doi.org/10.1109/CoDIT.2018.8394819
Publication status	Published - 13 Apr 2018
MoE publication type	A4 Article in a conference publication
Event	5th International Conference on Control, Decision and Information Technologies, CoDIT 2018 - The Grand Palace Hotel, Thessaloniki, Greece Duration: 10 Apr 2018 → 13 Apr 2018

Conference

Conference	5th International Conference on Control, Decision and Information Technologies, CoDIT 2018
Abbreviated title	CoDIT 2018
Country/Territory	Greece
City	Thessaloniki
Period	10/04/18 → 13/04/18

Funding

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.

Keywords

data mining
big data
open source
maintenance
CBM

Access to Document

10.1109/CoDIT.2018.8394819

Open Source Analytics Solutions for MaintenanceSubmitted manuscript, 268 KB

Cite this

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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{\textquoteright}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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author = "Erkki Jantunen and Jaime Campos and Pankaj Sharma and Mark McKay",

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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 et al.
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 proceeding › Conference article in proceedings › Scientific › peer-review

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PY - 2018/4/13

Y1 - 2018/4/13

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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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ER -

Open Source Analytics Solutions for Maintenance

Abstract

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Keywords

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MANTIS: Cyber Physical System based Proactive Collaborative Maintenance

Cite this

Open Source Analytics Solutions for Maintenance

Abstract

Conference

Funding

Keywords

Access to Document

Other files and links

Fingerprint

Projects

MANTIS: Cyber Physical System based Proactive Collaborative Maintenance

Cite this