## 3 Literature Survey  
### Big Data Approaches for General Industrial Applications  
Existing research into big data utilization for general industrial applications may be broadly generalized to contain valuable work and insight into the state of technology, current challenges, and methodologies or high-level frameworks for big-data analytical projects. The following section contains examples that, while not intended to be exhaustive, are representative of the body of literature on the subject. These examples are reviewed with specific interest in how they treat the research motivations from the human versus the architectural or technological dimension. [PERSON] et al. (2016) present an overview of machine learning in manufacturing, focusing specifically on advantages, challenges, and applications ([PERSON] et al., 2016). Of particular interest is a summary of several recent studies ([PERSON] et al., 2016; [PERSON] et al., 2017; [PERSON] et al., 2018) on the key challenges currently faced by the larger global manufacturing industry, with agreement on the following key challenges:  
* Adoption of advanced manufacturing technologies
* Growing importance of manufacturing of high value-added products
* Utilizing advanced knowledge, information management, and AI systems
* Sustainable manufacturing (processes) and products
* Agile and flexible enterprise capabilities and supply chains
* Innovation in products, services, and processes
* Close collaboration between industry and research to adopt new technologies
* New manufacturing paradigms. It is interesting to observe, in addition to what is listed, what is not listed. Specifically, these recent studies did not identify data dimensionality as a key challenge. In other words, while there is recognition that voluminous manufacturing data is collected, there is not universal agreement that this is a problem that needs to be addressed on the front end ([PERSON] et al., 2016); rather, employment of various machine learning techniques is proposed as a means to deal with it (Kal  
other processes. This observation is noteworthy, as it relates tangentially to the motivation for this literature review. One reason for the willingness to select machine learning algorithms that can handle high dimensionality may be a 'prisoner-of-the-moment' mentality. Analysts and data scientists perform real-world analyses to solve real-world problems, usually on a deadline imposed beyond their control. That deadline may be imposed by supervisors or it may be a function of outside constraints. Circumstances may not afford the luxury to step back, after completing the initial project, and thoroughly comb through the data to draw secondary conclusions about the nature of the input data. Rather, it is on to the next problem. [PERSON] et al. (2018) unpack the benefits and applications of deep learning for smart manufacturing, identifying benefits that include new visibility into operations for decision-makers and the availability of real-time performance measures and costs [35]. The authors provide, in addition to this practical information, a useful discussion on deep learning as a big-data analytical tool. In particular, they compare deep learning with traditional machine learning and offer three key distinctions between the two. Those distinctions are summarized in Table 1[35]. Note the distinction in feature learning. Deep learning models do not explicitly engineer and extract features. Rather, they are learned abstractly. This is both an advantage and a tradeoff. The blessing is that model performance is typically superior. The tradeoff is in the transparency, traceability, and front-end verifiability of results. The authors make an interesting observation, in that deep learning has shown itself to be most effective when it is applied to limited types of data and well-defined tasks [35]. This is notable in that conventional wisdom sometimes holds more data is better. Reducing the large data set to the most relevant subset of predictors may actually improve performance.