An evidential analytics for buried information in big data samples: Case study of semiconductor manufacturing

The big data samples are important source for analytics. However, its relevant/irrelevant information, unspecified variables/scales, noise/null, and so forth impose huge challenges on the analysis of relevance, feature, cause, and evaluation. This paper proposes an evidential analytics to disclose buried information in big data samples. Technically, it models memberships composed of relevant preference and replaces data with these priors. Its operations include generating analytics baselines, reducing variables, identifying sparse features, and inducing rules by taking advantage of evidence. In illustration, a case study of semiconductor manufacturing in UCI secom is presented. It discloses relevant signals, key factors, variables' thresholds, sparse characteristics, and causal effect of damages buried in normal samples. The contribution of this paper not only contains these achievements but provides priori data for inference. (C) 2019 Elsevier Inc. All rights reserved.