## 3 Literature Survey
### Big Data Approaches for General Industrial Applications
This speaks directly to the motivation for this review and demonstrates the importance of the question. Not only does the capability to reduce a feature set to only the most relevant features enable an organization to build and increase institutional knowledge about the data at its disposal, but it also may lead to superior model performance. Closely related, [PERSON] et al. (2018) provide a comprehensive look at data-driven smart manufacturing, providing a historical perspective on the evolution of manufacturing data, a development perspective on the lifecycle of big manufacturing data, and a framework envisioning the future of data in manufacturing [2]. An observation is that [PERSON] et al. also identify a gap and promising future research direction that aligns indirectly with the focus of this literature review: edge computing. Edge computing is, architecturally, an option for whittling down the volumes of production data into the core pieces that are truly meaningful and align with the key performance indicators (KPIs) of interest. Edge computing allows data to be analyzed at the \"edge\" of a network before being sent to a data center or cloud [36]. A related term, fog computing, was introduced by Cisco systems in 2014 and extends the cloud to be closer to devices that produce and act on IIOT data [37]. The distinction between the two concepts, as well as other emerging paradigms such as mobile edge computing (MEC) and mobile \begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Technique** & **Feature Learning** & **Model Construction** & **Model Training** \\ \hline
**Traditional Machine Learning** & Features are identified, engineered and extracted manually through domain expert knowledge. & Models typically have shallow structures (few hidden layers) and are data-driven using features. & Modules are trained step by step. \\ \hline
**Deep Learning** & Features are learned by transforming the data into abstract representations. & Models are end-to-end, high hierarchies with nonlinear combinations of numerous hidden layers. & Model parameters are trained jointly. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Distinction between traditional machine learning and deep learning. cloud computing (MCC) are not fully mature and are subject to overlap [38]. The commonality is that they represent means for an organization to operationalize the individual competencies that are the focus of this review. A final framework for general industrial application of big data is presented by [PERSON] and [PERSON] (2017), specifically in the form of a data science \"toolbox\" for manufacturing prediction tasks. The objective is to bridge the gap between machine learning research and practical needs [39]. Feature engineering is identified as an important step that must take place prior to deriving useful patterns from the input data, and a case study employs Kullback-Leibler divergence to reduce 968 numeric features to 150 and 2140 categorical features to 27. The preceding literature, summarized in Table 2 below, shows high-level analysis of trends and challenges. It also provides examples of methodologies and frameworks for applied big data analytics in manufacturing. A first observation is that there is not uniform agreement with regard to the question of dimensionality. At one extreme, the question is treated as a non-issue, to be handled by the machine learning algorithm selected. Other articles addressed the question at a high-level as important but always within the context of the larger problem-solving approach and not to the level of detail that would be useful to the data scientist. A second observation is that the approaches for predictive analytics in this section are geared less towards the detailed steps that an analyst might perform and more towards the infrastructure, architecture, and general data landscape that an organization should possess in order to have the capability to perform applied predictive analytics projects.