The excitement and buzz around big data (BD) has been so prevalent that it seems BD has become thought of as a panacea for solving all challenges. But there are in fact limitations to what BD can do, ...which has led to missed opportunities and potentially suboptimal problem‐solving and decision‐making. Hence the answer to the question posed in the title is an emphatic ‘Yes!’ The intentional strategic use of designed data collection (DDC) represents an opportunity to elevate how studies involving BD are planned, executed, and interpreted to deliver more satisfying solutions. In this paper, we outline eleven opportunities for integrating DDC strategies, including statistical design of experiments (DoE) and sampling techniques, with BD at all stages of the study. There are opportunities for building background understanding of the problem and improving the quality of the data via strategic planning before acquiring BD. In addition, incorporating DDC into the collection and processing of the actual BD, and strengthening conclusions or augmenting with supplementary designed data after BD also represent key opportunities to increase the impact of BD in the era of Industry 4.0 and Quality 4.0.
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4.
Input‐response space‐filling designs Lu, Lu; Anderson‐Cook, Christine M.
Quality and reliability engineering international,
December 2021, Volume:
37, Issue:
8
Journal Article
Peer reviewed
Open access
Traditional space‐filling designs are a convenient way to explore throughout an input space of flexible dimension and have design points close to any region where future predictions might be of ...interest. In some applications, there may be a model connecting the input factors to the response(s), which provides an opportunity to consider the spacing not only in the input space but also in the response space. In this paper, we present an approach for leveraging current understanding of the relationship between inputs and responses to generate designs that allow the experimenter to flexibly balance the spacing in these two regions to find an appropriate design for the experimental goals. Applications where good spacing of the observed response values include calibration problems where the goal is to demonstrate the adequacy of the model across the range of the responses, sensitivity studies where the outputs from a submodel may be used as inputs for subsequent models, and inverse problems where the outputs of a process will be used in the inverse prediction for the unknown inputs. We use the multi‐objective optimization method of Pareto fronts to generate multiple non‐dominated designs with different emphases on the input and response space‐filling criteria from which the experimenter can choose. The methods are illustrated through several examples and a chemical engineering case study.
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Space-filling designs continue to gain popularity for computer experiments. Uniformity of space-filling characteristics has been broadly sought after to provide good estimation and prediction ...abilities for a variety of complex models. This article presents case studies when additional information on the features of the underlying relationship may be leveraged for selecting alternative space-filling designs that offer improvements to meet specific experimental goals. Three types of nontraditional space-filling designs are illustrated to achieve different objectives to (1) allow varied density of design points across the input space, (2) obtain balanced performance on covering the input space and the range of the response values, and (3) effectively augment existing runs to achieve certain space-filling characteristic in a sequential experiment. The mechanics for implementing these design choices are described and their flexibility to adapt to other experimental scenarios is illustrated.
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The benefits of sequential design of experiments have long been described for both model‐based and space‐filling designs. However, in our experience, too few practitioners take advantage of the ...opportunity afforded by this approach to maximize the learning from their experimentation. By obtaining data sequentially, it is possible to learn from the early stages to inform subsequent data collection, minimize wasted resources, and provide answers for a series of objectives for the overall experiment. This paper provides methods and algorithms to create augmented distance‐based space‐filling designs, using both uniform and non‐uniform space‐filling strategies, that can be constructed at each stage based on information learned in earlier stages. We illustrate the methods with several examples that involve different initial data, types of space‐filing designs and experimental goals.
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Traditionally space‐filling designs have focused on the characteristics of the design in the input space ensuring uniform spread throughout the region. Input‐response space‐filling designs considered ...scenarios when having good spread throughout the range or region of the responses is also of interest. This paper acknowledges that there is typically uncertainty associated with the values of the response(s) and hence proposes a method, Input‐Response Space‐Filling Designs with Uncertainty (IRSFwU), to incorporate this into the design construction. The Pareto front of designs offers alternatives that balance input and response space filling, while prioritizing input combinations with lower associated response uncertainty. These lower uncertainty choices improve the chances of observing the desired response values. We describe the new approach with an uncertainty‐adjusted distance to measure the response space filling, the Pareto aggregate point exchange algorithm to populate the set of promising designs, and illustrate the method with three examples of different input and response relationships and dimensions.
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When optimizing an experimental design for good prediction performance based on an assumed second order response surface model, it is common to focus on a single optimality criterion, either ...G-optimality, for best worst-case prediction precision, or I-optimality, for best average prediction precision. In this article, we illustrate how using particle swarm optimization to construct a Pareto front of non-dominated designs that balance these two criteria yields some highly desirable results. In most scenarios, there are designs that simultaneously perform well for both criteria. Seeing alternative designs that vary how they balance the performance of G- and I-efficiency provides experimenters with choices that allow selection of a better match for their study objectives. We provide an extensive repository of Pareto fronts with designs for 17 common experimental scenarios for 2 (design size N = 6 to 12), 3 (N = 10 to 16) and 4 (N = 15, 17, 20) experimental factors. These, when combined with a detailed strategy for how to efficiently analyze, assess, and select between alternatives, provide the reader with the tools to select the ideal design with a tailored balance between G- and I-optimality for their own experimental situations.
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Carbon dioxide capture via solvent absorption in packed columns has emerged as a potential technology to mitigate coal‐fired power plant CO2 emissions. Parameters, including packing types, solvent ...properties, and operating conditions, could potentially affect the packed column CO2 capture efficiency. To understand the importance of those parameters and help packed column optimization, a design of experiments (DoEs) method was proposed to generate input parameter matrix. Combined with multiphase computational fluid dynamics (CFD), the random packed column parameter influence on the liquid holdup and interfacial area can be efficiently investigated. Surrogate‐based sensitivity analysis shows that the solvent flow rate and contact angle are key factors dictating liquid holdup and interfacial area. On the other hand, solvent viscosity has a marginal impact on the interfacial area. The sensitivity scores were calculated for each input parameter to guide the selection of dimensionless numbers for the liquid holdup and interfacial area correlation development.
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Live streaming is a unique form of media that creates a direct line of interaction between streamers and viewers. While previous research has explored the social motivations of those who stream and ...watch streams in the gaming community, there is a lack of research that investigates intimate self-disclosure in this context, such as discussing sensitive topics like mental health on platforms such as Twitch.tv. This study aims to explore discussions about mental health in gaming live streams to better understand how people perceive discussions of mental health in this new media context. The context of live streaming is particularly interesting as it facilitates social interactions that are masspersonal in nature: the streamer broadcasts to a larger, mostly unknown audience, but can also interact in a personal way with viewers. In this study, we interviewed Twitch viewers about the streamers they view, how and to what extent they discuss mental health on their channels in relation to gaming, how other viewers reacted to these discussions, and what they think about live streams, gaming-focused or otherwise, as a medium for mental health discussions. Through these interviews, our team was able to establish a baseline of user perception of mental health in gaming communities on Twitch that extends our understanding of how social media and live streaming can be used for mental health conversations. Our first research question unraveled that mental health discussions happen in a variety of ways on Twitch, including during gaming streams, Just Chatting talks, and through the stream chat. Our second research question showed that streamers handle mental health conversations on their channels in a variety of ways. These depend on how they have built their channel, which subsequently impacts how viewers perceive mental health. Lastly, we learned that viewers' reactions to mental health discussions depend on their motivations for watching the stream such as learning about the game, being entertained, and more. We found that more discussions about mental health on Twitch led to some viewers being more cautious when talking about mental health to show understanding.
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