## 3 Literature Survey
### Frameworks for Data Reduction  
The third and final category of literature that this review will examine focuses on techniques or approaches specifically for data reduction, which includes feature reduction/selection and instance reduction/selection. There exists a substantial body of influential data preprocessing algorithms for missing values imputation, noise filtering, dimensionality reduction, instance reduction, and treatment of data for imbalanced processing [77]. Specific algorithms for feature selection include Las Vegas Filter/Wrapper [78], Mutual Information Feature Selection [79], Relief [80], and Minimum Redundancy Maximum Relevance (mRMR) [81]. Specific algorithms for instance reduction include condensed \begin{table}
\begin{tabular}{l l l} \hline \hline
**Authors(s)** & **Focus** & **Explicit Data** \\  & & **Reduction Step** \\ \hline [PERSON] et al. [40] & Enterprise-level architecture; methodology to & No \\  & address class imbalance & \\ \hline [PERSON] and [PERSON] [43] & Enterprise-level architecture & No \\ \hline [PERSON] and [PERSON] [46] & Case study: Machine health states—DBN & No \\ \hline [PERSON] et al. [48] & Case study: Fault characterization—DNN & No \\ \hline [PERSON] et al. [50] & Case study: Fault signal identification—SVM &
\begin{tabular}{l} Discussed, not \\ implemented \\ \end{tabular} \\ \hline [PERSON] et al. [56] & Methodology: Information fusion to reconcile & No \\  & conflicting evidence in fault detection & \\ \hline [PERSON] et al. [58] & Case study: Yield enhancement—Bayesian & Yes \\  & inference & \\ \hline [PERSON] [59] & Enterprise-level architecture; Case study: Fault & No \\  & detection and classification—SVR, RBF, DBL-DL & \\ \hline [PERSON] et al. [64] & Enterprise-level architecture; Methodology: & No \\  & Real-time and offline components; Case study: & \\  & Fault prediction—Neural Network & \\ \hline [PERSON] \& Ramadoss [65] & Enterprise-level architecture & Yes \\ \hline [PERSON] and [PERSON] [67] & Enterprise-level architecture; Simulated proof of & No \\  & concept case study: Fault prediction for shop & \\  & floor scheduling & \\ \hline [PERSON] et al. [68] & Case study: Lubrication defects in cold forging & No \\  & process—NN & \\ \hline [PERSON] and [PERSON] [69] & Ductile cast iron quality—NN & No \\ \hline [PERSON] et al. [70] & Micro-milling parameter optimization—NN & No \\ \hline [PERSON] et al. [71] & Alloy flow behavior-NN & No \\ \hline [PERSON] et al. [72] & Dimensional error in precision machining—NN & No \\ \hline [PERSON] et al. [73] & High speed machining of moulds & No \\ \hline [PERSON] and [PERSON] [76] & Manufacturing freeform surfaces & No \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Big data approaches for specific industrial applications. nearest neighbor (CNN) [82], edited nearest neighbor (ENN) [83], decremental reduction by ordered projections (DROP) [84], and iterative case filtering (ICF) [85]. The interest in the ensuing articles reviewed in this section is in their suitability for application to the CE. To this end, the domain in which the articles implement any applied case studies is also examined. It will be observed that the reviewed articles contain tasks that fall within Step 3 of the data source selection methodology outlined in [86] and broadly fall into one of three categories: sampling reduction, feature reduction, or instance reduction. Sampling reduction applies to contexts such as optical inspection or reengineering, where there is a need to obtain information for an entire component or surface. If that information may be obtained using fewer samples, then benefits in cost or efficiency follow. Instance reduction applies to contexts in which large numbers of data points are collected for a relatively smaller set of attributes or features. Feature selection is the process of reducing the number of attributes or columns to be input into a machine learning model for training. [PERSON], et al.