Abstract
| 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 |
| Number of pages | 6 |
| ISBN (Electronic) | 978-1-5386-5065-3 |
| DOIs | |
| 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 | Greece |
| City | Thessaloniki |
| Period | 10/04/18 → 13/04/18 |
Fingerprint
Keywords
- data mining
- big data
- open source
- maintenance
- CBM
Cite this
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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 proceeding › Conference article in proceedings › Scientific › peer-review
TY - GEN
T1 - Open Source Analytics Solutions for Maintenance
AU - Jantunen, Erkki
AU - Campos, Jaime
AU - Sharma, Pankaj
AU - McKay, Mark
N1 - Lisätty proc. tiedot 28.9.18
PY - 2018/4/13
Y1 - 2018/4/13
N2 - 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.
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.
KW - data mining
KW - big data
KW - open source
KW - maintenance
KW - CBM
UR - http://www.scopus.com/inward/record.url?scp=85050221189&partnerID=8YFLogxK
U2 - 10.1109/CoDIT.2018.8394819
DO - 10.1109/CoDIT.2018.8394819
M3 - Conference article in proceedings
SP - 688
EP - 693
BT - Proceedings of the 5th International Conference on Control, Decision and Information Technologies
PB - IEEE Institute of Electrical and Electronic Engineers
ER -