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This paper introduces a multi-dimensional mechanism to analyse the importance of components in a system and the their influences on the reliability from four ...
Abstract—In a component-based system, both the reliability and topological location of each component contribute to the software architecture reliability.
Principal component analysis (PCA) is a dimensionality reduction and machine learning method used to simplify a large data set into a smaller set.
Functional principal component analysis is one the most commonly employed approaches in functional/longitudinal data analysis and we extend it to conduct ...
Apr 15, 2022 · A reliability analysis framework is proposed for multi-component systems. The stochastic dependency among components is captured by the factor analysis.
Component-based software development proposes building systems by using pre-existing components, to reduce development time, costs and risks and to improve ...
It is often used to reduce the dimensionality of the dataset so you can identify features and patterns of the data. For example, in multivariate analysis, PCA ...
Missing: Importance Mechanism Based Systems.
Principal component analysis (PCA) is a linear dimensionality reduction technique with applications in exploratory data analysis, visualization and data ...
Jan 1, 2016 · A well-known method is to compute the Birnbaum component importance (BCI) of every component, then prioritize reliability improvement efforts on ...
Dec 12, 2023 · This paper proposes a multi-objective optimization Microservices framework that takes into account the security mechanism, Define the fitness function, define ...