scholarly journals A Design Space for Visualization Onboarding in Data-Driven Stories

2021 ◽  
Author(s):  
Morteza Asgari ◽  
Thomas Hurtut
Keyword(s):  
Design Process ◽  
Design Space ◽  
Data Driven ◽  
Level Of Abstraction ◽  
Narrative Format

Data-Driven Stories (DDS) are stories that combine text and data portrayed as visualization in a narrative format. They are among the popular ways of communicating information by online medias nowadays. For DDS authors and designers, it's important to minimize the risk of misinterpreting visualizations by their readers. Visualization onboarding, embedding knowledge and guidance have been meant to provide adequate support for readers to understand visualizations as they progress through DDS. Onboarding is a continuous mechanism which involves various DDS elements and interactions on each step. Several previous studies attempted to identify and classify storytelling techniques in DDS. While these techniques prospect a satisfactory communication, it's not clear how they can be applied to facilitate the visualization understanding throughout the story. They rather conceptualized different aspects of storytelling individually, and as such, the chronology of onboarding steps has been missed. Although their techniques and design spaces represent a tangible level of abstraction, they will not benefit authors in the story design process. Authors either rely on their guess work or mimic previous DDS to accommodate support in their DDS scenarios. In this project, our overall goal is to propose a multidimensional design space for onboarding techniques in DDS that can benefit to authors during their design process.

Shared-Gaussian Process: Learning Interpretable Shared Hidden Structure Across Data Spaces for Design Space Analysis and Exploration

2020 ◽  
Vol 142 (8) ◽  
Author(s):  
Wei Xing ◽  
Shireen Y. Elhabian ◽  
Vahid Keshavarzzadeh ◽  
Robert M. Kirby
Keyword(s):  
Gaussian Process ◽  
Design Process ◽  
Latent Variable ◽  
Design Space ◽  
Mathematical Formulation ◽  
Correlated Data ◽  
Data Driven ◽  
Design Solution ◽  
Variable Model ◽  
Trade Offs

Abstract An industrial design process is often highly iterative. With unclear relationships between the quantity of interest (QoI) trade-offs and the design solution, the definition of the cost function usually undergoes several modifications that mandate a continued interaction between the designer and the client to encode all design and mission requirements into an optimization-friendly mathematical formulation. Such an iterative process is time consuming and computationally expensive. An efficient way to accelerate this process is to derive data-driven mappings between the design/mission and QoI spaces to provide visual insights into the interactions among different QoIs as related to their corresponding simulation parameters. In this paper, we propose Shared-Gaussian process (GP), a generative model for the design process that is based on a Gaussian process latent variable model. Shared-GP learns correlations within and across multiple, but implicitly correlated, data spaces considered in the design process (i.e., the simulation parameter space, the design space, and the QoI spaces) to provide data-driven mappings across these data spaces via efficient inference. Shared-GP also provides a structured low-dimensional representation shared among data spaces (some of which are of very high dimension) that the designer can use to efficiently explore the design space without the need for costly simulations.

Data-Driven Design Space Exploration and Exploitation for Design for Additive Manufacturing

2019 ◽  
Vol 141 (10) ◽  
Author(s):  
Yi Xiong ◽  
Pham Luu Trung Duong ◽  
Dong Wang ◽  
Sang-In Park ◽  
Qi Ge ◽  
...  
Keyword(s):  
Additive Manufacturing ◽  
Design Process ◽  
Sampling Method ◽  
Design Space ◽  
Data Driven ◽  
Design Stage ◽  
Design For Additive Manufacturing ◽  
Detailed Design ◽  
Embodiment Design ◽  
Ankle Brace

Recently, design for additive manufacturing has been proposed to maximize product performance through the rational and integrated design of the product, its materials, and their manufacturing processes. Searching design solutions in such a multidimensional design space is a challenging task. Notably, no existing design support method is both rapid and tailored to the design process. In this study, we propose a holistic approach that applies data-driven methods in design search and optimization at successive stages of a design process. More specifically, a two-step surrogate model-based design method is proposed for the embodiment and detailed design stages. The Bayesian network classifier is used as the reasoning framework to explore the design space in the embodiment design stage, while the Gaussian process regression model is used as the evaluation function for an optimization method to exploit the design space in detailed design. These models are constructed based on one dataset that is created by the Latin hypercube sampling method and then refined by the Markov Chain Monte Carlo sampling method. This cost-effective data-driven approach is demonstrated in the design of a customized ankle brace that has a tunable mechanical performance by using a highly stretchable design concept with tailored stiffnesses.

scholarly journals Design space exploration for flexibility assessment and decision making support in integrated industrial building design

2021 ◽  
Author(s):  
Julia Reisinger ◽  
Maximilian Knoll ◽  
Iva Kovacic
Keyword(s):  
Decision Making ◽  
Design Process ◽  
Design Space Exploration ◽  
Design Space ◽  
Parametric Design ◽  
Space Exploration ◽  
Building Design ◽  
Assessment Tools ◽  
Industrial Building ◽  
Industrial Buildings

AbstractIndustrial buildings play a major role in sustainable development, producing and expending a significant amount of resources, energy and waste. Due to product individualization and accelerating technological advances in manufacturing, industrial buildings strive for highly flexible building structures to accommodate constantly evolving production processes. However, common sustainability assessment tools do not respect flexibility metrics and manufacturing and building design processes run sequentially, neglecting discipline-specific interaction, leading to inflexible solutions. In integrated industrial building design (IIBD), incorporating manufacturing and building disciplines simultaneously, design teams are faced with the choice of multiple conflicting criteria and complex design decisions, opening up a huge design space. To address these issues, this paper presents a parametric design process for efficient design space exploration in IIBD. A state-of-the-art survey and multiple case study are conducted to define four novel flexibility metrics and to develop a unified design space, respecting both building and manufacturing requirements. Based on these results, a parametric design process for automated structural optimization and quantitative flexibility assessment is developed, guiding the decision-making process towards increased sustainability. The proposed framework is tested on a pilot-project of a food and hygiene production, evaluating the design space representation and validating the flexibility metrics. Results confirmed the efficiency of the process that an evolutionary multi-objective optimization algorithm can be implemented in future research to enable multidisciplinary design optimization for flexible industrial building solutions.

The Impact of Geometric Variation on Compressor Two-Dimensional Incidence Range

2014 ◽  
Vol 137 (2) ◽  
Author(s):  
Martin N. Goodhand ◽  
Robert J. Miller ◽  
Hang W. Lung
Keyword(s):  
Design Process ◽  
Design Space ◽  
Leading Edge ◽  
Critical Value ◽  
Two Dimensional ◽  
Suction Surface ◽  
Design Intent ◽  
Manufacture Process ◽  
The Impact ◽  
Geometric Variation

An important question for a designer is how, in the design process, to deal with the small geometric variations which result from either the manufacture process or in-service deterioration. For some blade designs geometric variations will have little or no effect on the performance of a row of blades, while in others their effects can be significant. This paper shows that blade designs which are most sensitive are those which are susceptible to a distinct switch in the fluid mechanisms responsible for limiting blade performance. To demonstrate this principle, the sensitivity of compressor 2D incidence range to manufacture variations is considered. Only one switch in mechanisms was observed, the onset of flow separation at the leading edge. This switch is only sensitive to geometric variations around the leading edge, 0–3% of the suction surface. The consequence for these manufacture variations was a 10% reduction in the blade's positive incidence range. For this switch, the boundary in the design space is best defined in terms of the blade pressure distribution. Blade designs where the acceleration exceeds a critical value just downstream of the leading edge are shown to be robust to geometric variation. Two historic designs, supercritical blades and blades with sharp leading edges, though superior in design intent, are shown to sit outside this robust region and thus, in practice, perform worse. The improved understanding of the robust, region of the design space is then used to design a blade capable of a robust, 5% increase in operating incidence range.

A Structured Design Methodology and Metadesigner: A System Shell Concept for Computer Aided Creative Design

1989 ◽  
Author(s):  
C. R. Liu ◽  
J. C. Trappey
Keyword(s):  
Design Process ◽  
Design Methodology ◽  
Design Space ◽  
Structured Design ◽  
Programming Paradigm ◽  
Knowledge Based ◽  
Design Structure ◽  
Hierarchical Nature ◽  
Design Options ◽  
Human Designer

Abstract This paper discusses the concept of managing the design process using Objected Oriented Programming Paradigm. A software system shell, called MetaDesigner is being developed for aiding the human designer to create new designs, based on the hierarchical nature of the design space. This system shell is intended to have the following capabilities: (1) interactive and system-guided design process to analyze design structure and to characterize design options, (2) to provide interactive and system-guided knowledge acquisition, classification, and retrieval to achieve machine learning, and (3) to build a flexible and forever expandable structure for knowledge-based system implementation.

Multidimensional Design Methods for Data Warehousing

2011 ◽  
pp. 78-105 ◽  
Author(s):  
Oscar Romero ◽  
Alberto Abelló
Keyword(s):  
Data Warehouse ◽  
Design Process ◽  
Data Warehousing ◽  
Design Methods ◽  
Data Sources ◽  
Data Driven ◽  
Main Research ◽  
Multidimensional Modeling ◽  
Warehouse Design ◽  
Warehousing Systems

In the last years, data warehousing systems have gained relevance to support decision making within organizations. The core component of these systems is the data warehouse and nowadays it is widely assumed that the data warehouse design must follow the multidimensional paradigm. Thus, many methods have been presented to support the multidimensional design of the data warehouse.The first methods introduced were requirement-driven but the semantics of the data warehouse (since the data warehouse is the result of homogenizing and integrating relevant data of the organization in a single, detailed view of the organization business) require to also consider the data sources during the design process. Considering the data sources gave rise to several data-driven methods that automate the data warehouse design process, mainly, from relational data sources. Currently, research on multidimensional modeling is still a hot topic and we have two main research lines. On the one hand, new hybrid automatic methods have been introduced proposing to combine data-driven and requirement-driven approaches. These methods focus on automating the whole process and improving the feedback retrieved by each approach to produce better results. On the other hand, some new approaches focus on considering alternative scenarios than relational sources. These methods also consider (semi)-structured data sources, such as ontologies or XML, that have gained relevance in the last years. Thus, they introduce innovative solutions for overcoming the heterogeneity of the data sources. All in all, we discuss the current scenario of multidimensional modeling by carrying out a survey of multidimensional design methods. We present the most relevant methods introduced in the literature and a detailed comparison showing the main features of each approach.

Interactive Tables

2010 ◽  
pp. 741-762
Author(s):  
Michael Haller ◽  
Mark Billinghurst
Keyword(s):  
Design Process ◽  
Design Space ◽  
Three Dimensional ◽  
Information Space ◽  
Digital Data ◽  
User Requirements ◽  
Digital Paper ◽  
Digital Pen ◽  
Dimensional Models ◽  
Three Dimensional Models

Interactive tables are becoming increasingly popular. In this chapter, we describe a collaborative tabletop environment that is designed for brainstorming meetings. After describing the user requirements, we demonstrate different possible solutions for both the display and the tracking implementation, and summarize related work. Finally, we conclude with a more detailed description of the Shared Design Space. Using a digital pen, participants can annotate not only virtual paper, but also real printouts. By integrating both forms of physical and digital paper, we combine virtual and real drawings, three-dimensional models, and digital data in a single information space. We discuss the unique way that we have integrated these devices and how they can be used efficiently during a design process.

Negotiating human engagement and the fixity of computational design: Toward a performative design space for the differently-abled bodymind

2020 ◽  
Vol 18 (2) ◽  
pp. 174-193
Author(s):  
Sean Ahlquist
Keyword(s):  
Environmental Factors ◽  
Structural Optimization ◽  
Design Process ◽  
Design Space ◽  
Computational Design ◽  
Social Nature ◽  
And Performance ◽  
The Moment ◽  
Cultural Entity

Computational design affords agency: the ability to orchestrate the material, spatial, and technical architectural system. In this specific case, it occurs through enhanced, authored means to facilitate making and performance—typically driven by concerns of structural optimization, material use, and responsivity to environmental factors—of an atmospheric rather than social nature. At issue is the positioning of this particular manner of agency solely with the architect auteur. This abruptly halts—at the moment in which fabrication commences—the ability to amend, redefine, or newly introduce fundamentally transformational constituents and their interrelationships and, most importantly, to explore the possibility for extraordinary outcomes. When the architecture becomes a functional, social, and cultural entity, in the hands of the idealized abled-bodied user, agency—especially for one of an otherly body or mind—is long gone. Even an empathetic auteur may not be able to access the motivations of the differently-abled body and neuro-divergent mind, effectively locking the constraints of the design process, which creates an exclusionary system to those beyond the purview of said auteur. It can therefore be deduced that the mechanisms or authors of a conventional computational design process cannot eradicate the exclusionary reality of an architectural system. Agency is critical, yet a more expansive terminology for agent and agency is needed. The burden to conceive of capacities that will always be highly temporal, social, unpredictable, and purposefully unknown must be shifted far from the scope of the traditional directors of the architectural system. Agency, and who it is conferred upon, must function in a manner that dissolves the distinctions between the design, the action of designing, the author of design, and those subjected to it.

Design Space Exploration of Split-Path Data Driven Dynamic Full Adder

2010 ◽  
Vol 6 (4) ◽  
pp. 469-481 ◽  
Author(s):  
Sohan Purohit ◽  
Marco Lanuzza ◽  
Martin Margala
Keyword(s):  
Design Space Exploration ◽  
Design Space ◽  
Space Exploration ◽  
Full Adder ◽  
Data Driven

An Integrated System for the Aerodynamic Design of Compression Systems—Part I: Development

2010 ◽  
Vol 133 (1) ◽  
Author(s):  
Tiziano Ghisu ◽  
Geoffrey T. Parks ◽  
Jerome P. Jarrett ◽  
P. John Clarkson
Keyword(s):  
Gas Turbine ◽  
Design Process ◽  
Design Space ◽  
Integrated Design ◽  
Complex Problem ◽  
Design Approach ◽  
Turbine Engines ◽  
Gas Turbine Engines ◽  
Final Design ◽  
Multidisciplinary System

The design of gas turbine engines is a complex problem. This complexity has led to the adoption of a modular design approach, in which a conceptual design phase fixes the values for some global parameters and dimensions in order to facilitate the subdivision of the overall task into a number of simpler subproblems. This approach, while making a complex problem more tractable, necessarily has to rely on designer experience and simple evaluations to specify these process-intrinsic constraints at a point in the design process where very little knowledge about the final design exists. Later phases of the design process, using higher-fidelity tools but acting on a limited region of the design space, can only refine an already established design. While substantial improvements in performance have been possible with the current approach, further gains are becoming increasingly hard to achieve. A gas turbine is a complex multidisciplinary system: a more integrated design approach can facilitate a better exploitation of the trade-offs between different modules and disciplines, postponing the setting of these critical interface parameters (such as flow areas, radii, etc.) to a point where more information exists, reducing their impact on the final design. In the resulting large, possibly multimodal, highly constrained design space, and with a large number of objectives to be considered simultaneously, finding an optimal solution by simple trial-and-error can prove extremely difficult. A more intelligent search approach, in which a numerical optimizer takes the place of the human designer in seeking optimal designs, can enable the design space to be explored significantly more effectively, while also yielding a substantial reduction in development times thanks to the automation of the design process. This paper describes the development of a system for the integrated design and optimization of gas turbine engines, linking a metaheuristic optimizer to a geometry modeler and to evaluation tools with different levels of fidelity. In recognition of the substantial increase in design space size required by the integrated approach, an improved parameterization based on the concept of principal components’ analysis was implemented, allowing a rotation of the design space along its most significant directions and a reduction in its dimensionality, proving essential for a faster and more effective exploration of the design space.

Sign in / Sign up

Export Citation Format

Share Document