Developing Data Mining-Based Prognostic Models for CF-18 Aircraft

Zaluski, Marvin; Létourneau, Sylvain; Bird, Jeff; Yang, Chunsheng

doi:10.1115/1.4002812

Cited by 16 publications

(1 citation statement)

References 8 publications

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“…The next most popular research area is manufacturing/engineering with 10 case studies. The central topic here is high-technology manufacturing, for example, semi-conductors associated-study of Chien, Diaz & Lan (2014), and various complex prognostics case studies in rail, aerospace domains (Létourneau et al, 2005;Zaluski et al, 2011) concentrated on failure predictions. These are complemented by studies on equipment fault and failure predictions and maintenance (Kumar, Shankar & Thakur, 2018;Kang et al, 2017;Wang, 2017) as well as monitoring system (García et al, 2017).…”

Section: Manufacturing and Engineeringmentioning

confidence: 99%

Adaptations of data mining methodologies: a systematic literature review

Plotnikova¹,

Dumas²,

Milani³

2020

PeerJ Computer Science

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The use of end-to-end data mining methodologies such as CRISP-DM, KDD process, and SEMMA has grown substantially over the past decade. However, little is known as to how these methodologies are used in practice. In particular, the question of whether data mining methodologies are used ‘as-is’ or adapted for specific purposes, has not been thoroughly investigated. This article addresses this gap via a systematic literature review focused on the context in which data mining methodologies are used and the adaptations they undergo. The literature review covers 207 peer-reviewed and ‘grey’ publications. We find that data mining methodologies are primarily applied ‘as-is’. At the same time, we also identify various adaptations of data mining methodologies and we note that their number is growing rapidly. The dominant adaptations pattern is related to methodology adjustments at a granular level (modifications) followed by extensions of existing methodologies with additional elements. Further, we identify two recurrent purposes for adaptation: (1) adaptations to handle Big Data technologies, tools and environments (technological adaptations); and (2) adaptations for context-awareness and for integrating data mining solutions into business processes and IT systems (organizational adaptations). The study suggests that standard data mining methodologies do not pay sufficient attention to deployment issues, which play a prominent role when turning data mining models into software products that are integrated into the IT architectures and business processes of organizations. We conclude that refinements of existing methodologies aimed at combining data, technological, and organizational aspects, could help to mitigate these gaps.

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