Change search
ReferencesLink to record
Permanent link

Direct link
Big Data Mining in eMaintenance: An Overview
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
2014 (English)In: Proceedings of the 3rd international workshop and congress on eMaintenance: June 17-18 Luleå, Sweden : eMaintenance, Trends in technologies & methodologies, challenges, possibilites and applications / [ed] Uday Kumar; Ramin Karim; Aditya Parida; Philip Tretten, Luleå: Luleå tekniska universitet, 2014, 159-170 p.Conference paper (Refereed)
Abstract [en]

Maintenance related data are tending to be increasingly huge involume, rapid in velocity and vast in variety. Data with thesecharacteristics bring new challenges with respect to data analysisand data mining, which requires new approaches andtechnologies. In industry, related research and applications, somecontributions have been provided to utilize Big Data technologiesfor extraction of information through pattern recognitionmechanisms via eMaintenance solutions. Today, the existingcontributions are not enabling a holistic approach for maintenancedata analysis and therefore are insufficient. However, theimmense value hidden inside the Big Data in eMaintenance isarousing more and more attention from both academia andindustry. Hence, this paper aims to explore eMaintenancesolutions for maintenance decision-making through utilization ofBig Data technologies and approaches. The paper discusses BigData mining in eMaintenance through a general manner byemploying one of the widely accepted frameworks with the nameof Cross Industry Standard Process for Data Mining (CRISPDM).In addition, the paper outlines features of maintenance dataand investigates six sub-processes (i.e. business understanding,data understanding, data preparation, modeling, evaluation anddeployment) of data mining applications defined by CRISP-DMwithin the domain of eMaintenance.

Place, publisher, year, edition, pages
Luleå: Luleå tekniska universitet, 2014. 159-170 p.
Research subject
Operation and Maintenance
URN: urn:nbn:se:ltu:diva-35850Local ID: a89e2a56-a39c-4584-8e2f-4fe67b17df96ISBN: 978-91-7439-973-8ISBN: 978-91-7439-973-8 (PDF)OAI: diva2:1009104
International Workshop and Congress on eMaintenance : 17/06/2014 - 18/06/2014
Godkänd; 2014; 20140623 (andbra)Available from: 2016-09-30 Created: 2016-09-30Bibliographically approved

Open Access in DiVA

fulltext(21227 kB)19 downloads
File information
File name FULLTEXT01.pdfFile size 21227 kBChecksum SHA-512
Type fulltextMimetype application/pdf

Search in DiVA

By author/editor
Zhang, LiangweiKarim, Ramin
By organisation
Operation, Maintenance and Acoustics

Search outside of DiVA

GoogleGoogle Scholar
Total: 19 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

Total: 78 hits
ReferencesLink to record
Permanent link

Direct link