## 3 Literature Survey
### Frameworks for Data Reduction
(2016) propose an enterprise-level data reduction framework for value creation in sustainable enterprises, which, while not contextualized to manufacturing, is easily extendable to this domain. The framework considers a traditional five-layer architecture for big data systems and adds three data reduction layers [87]. The first layer for local data reduction is intended for use in mobile devices to collect, preprocess, analyze, and store knowledge patterns. This physical layer can easily be conceptually translated to the CE. The second layer, for collaborative data reduction, is situated prior to the cloud level, with edge computing servers executing analytics to identify knowledge patterns. Note that \"edge computing\" may be referred to as \"fog computing\" in some cases [88]. This step will exist in varying degrees in the CE depending on the maturity of the process or organization. In the context of user Internet of Things (IoT) mobile data, as initially presented in the paper, there exists a body of data that must automatically be discarded in accordance with external constrains such as privacy laws. This brings a practical purpose to this initial filtration layer. In smart manufacturing, the physical layer represents IIOT machine or production data, all of which might theoretically harbor some purpose. It may not be prudent to automatically discard chunks of data until it has been definitively determined that there is little risk in doing so. Finally, a layer for remote data reduction is added to aggregate the knowledge patterns from edge servers that are then distributed to cloud data centers for data applications to access and further analyze [87]. It should be noted that this framework is at the institutional level and not at the level of the data scientist. The data reduction layers are presented as automated processes applied to the raw source data and not dependent on a specific project or problem of interest. At the data scientist level, a second point-based data reduction scenario is presented in [89], in which [PERSON] and [PERSON] (2011) develop a data reduction algorithm for 3D surface points for use in reverse engineering. In reverse engineering, data is captured from an existing surface on the order of millions of scanned points. There are challenges associated with volume of data, and there are challenges in the form of increased error associated with removing data. The data reduction algorithm is based on Hausdorff distance and works by first collecting a set of 3D point data from a surface using an optical device such as a laser scanner, iterating through the set of points, and determining if a point can be removed without causing the local estimation of surface characteristics to fall out of tolerance. This is done by comparing shape pre- and post-removal. The procedure is tested on an idealized aircraft wing but is extendable to any manner of reverse engineering that employs 3D measurement data. It is possible that this could also be extended to inspection-type applications, but the challenge is that the end-state number of required data points will be dependent on the nature of the surface. Additionally, it is not certain that Hausdorff distance would be the appropriate metric for other contexts such as automated optical inspection. Considering data reduction with respect to the set of features to be used for model training, [PERSON] et al. (2016) propose a feature selection approach based on simulated annealing and apply it to a case study for detecting denial of service attacks [90]. This approach is similar to [91], which uses the same data set but a different local search algorithm. The model starts with a randomly-generated set of features to include, trains a model on that set of features, and tests it by way of some pre-designated machine learning technique. The case study is a classification problem, and so examples used include SVM, multi-layer perceptron (MLP), and naive Bayes classifier (NBC).