What are Table Cartograms Good for Anyway? An Algebraic Analysis

McNutt, Andrew

doi:10.1111/cgf.14289

Cited by 16 publications

(13 citation statements)

References 68 publications

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“…", or "What is meant by data?" A notable experiment in this regard is Pu et al's [88] use of Algebraic Visualization [51,72] as a sibling to CDN, suggesting the applicability of visualization theory to DSL evaluation. JSON DSLs have been described as being intended for use by endusers [73].…”

Section: Discussionmentioning

confidence: 99%

No Grammar to Rule Them All: A Survey of JSON-style DSLs for Visualization

McNutt¹

2022

Preprint

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There has been substantial growth in the use of JSON-based grammars, as well as other standard data serialization languages, to create visualizations. Each of these grammars serves a purpose: some focus on particular computational tasks (such as animation), some are concerned with certain chart types (such as maps), and some target specific data domains (such as ML). Despite the prominence of this interface form, there has been little detailed analysis of the characteristics of these languages. In this study, we survey and analyze the design and implementation of 57 JSON-style DSLs for visualization. We analyze these languages supported by a collected corpus of examples for each DSL (consisting of 4395 instances) across a variety of axes organized into concerns related to domain, conceptual model, language relationships, affordances, and general practicalities. We identify tensions throughout these areas, such as between formal and colloquial specifications, among types of users, and within the composition of languages. Through this work, we seek to support language implementers by elucidating the choices, opportunities, and tradeoffs in visualization DSL design.

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Section: Discussionmentioning

confidence: 99%

No Grammar to Rule Them All: A Survey of JSON-style DSLs for Visualization

McNutt¹

2022

Preprint

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“…Bronstein et al [16] recently proposed a unifying mathematical framework to exploit these regularities using geometric principles of invariance and equivariance. Kindlmann et al [53] and McNutt [54] proposed similar frameworks for studying visualizations. To study models and their inductive biases, we extrapolate the algebraic visualization design framework [53], [54] to reason model behavior by analyzing intrinsic symmetries/asymmetries between their inputs and outputs (see Figure 3).…”

Section: Formalizing Input-output Relationsmentioning

confidence: 99%

“…Kindlmann et al [53] and McNutt [54] proposed similar frameworks for studying visualizations. To study models and their inductive biases, we extrapolate the algebraic visualization design framework [53], [54] to reason model behavior by analyzing intrinsic symmetries/asymmetries between their inputs and outputs (see Figure 3). Specifically, • α is an operator applied to the input with respect to control variables, such as noise or rotation.…”

Section: Formalizing Input-output Relationsmentioning

confidence: 99%

“…The user would likely perform multiple explorations increasing the support required from the VA system. Similar to the triplet model of McNutt [54], each user question focuses on understanding one or more of the 3Ws by fixing and varying the other components. Table 1 shows how example questions can be placed in the context of focus areas, and structured with the triplet model using the 3Ws.…”

Section: Kahng Et Al [13]mentioning

confidence: 99%

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The Transform-and-Perform Framework: Explainable Deep Learning Beyond Classification

Prasad

Sloun

Elzen

et al. 2024

IEEE Trans. Visual. Comput. Graphics

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In recent years, visual analytics (VA) has shown promise in alleviating the challenges of interpreting black-box deep learning (DL) models. While the focus of VA for explainable DL has been mainly on classification problems, DL is gaining popularity in high-dimensional-to-high-dimensional (H-H) problems such as image-to-image translation. In contrast to classification, H-H problems have no explicit instance groups or classes to study. Each output is continuous, high-dimensional, and changes in an unknown non-linear manner with changes in the input. These unknown relations between the input, model and output necessitate the user to analyze them in conjunction, leveraging symmetries between them. Since classification tasks do not exhibit some of these challenges, most existing VA systems and frameworks allow limited control of the components required to analyze models beyond classification. Hence, we identify the need for and present a unified conceptual framework, the Transform-and-Perform framework (T&P), to facilitate the design of VA systems for DL model analysis focusing on H-H problems. T&P provides guidelines to structure and identify workflows and analysis strategies to design new VA systems, and understand existing ones to uncover potential gaps for improvements. The goal is to aid the creation of effective VA systems that support the structuring of model understanding and identifying actionable insights for model improvements. We highlight the growing need for new frameworks like T&P with a real-world image-to-image translation application. We also illustrate how T&P effectively supports the understanding and identifying potential gaps in existing VA systems.

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“…While such queries are constructed similarly to relations in our approach, the former are driven through cross-filtering on attribute relationship graphs, while ours draws from rules heavily inspired by Kosslyn's principles [19] and Kindlmann and Scheidegger's algebraic framework [18]. The latter framework has been used to identify effective visualization types for certain user tasks, e.g., table cartograms [25]. Kim et al characterize responsive visualization strategies via their targets, i.e., element(s) of a design that change, and actions, i.e., how element(s) are changed [16].…”

Section: Related Workmentioning

confidence: 99%

Semantic Snapping for Guided Multi-View Visualization Design

Kristiansen¹,

Garrison²,

Brückner³

2021

Preprint

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Fig. 1. Semantic snapping allows the user to perform iterative operations to improve the compactness and consistency of a multi-view visualization. Underlying algebraic rules called relations define the available operations for each iteration. In this example showing the 2016 US Election poll percentages and pollsters, from our initial composition we (1) identify a confuser relation between the bottom left and rightmost views showing the same color (red). We differentiate these views by selecting green as the fill color for the scatter plot. We next identify a multiples relation in the two left views. We resolve this through one of two integration operations. (2*) Overlay produces an unsatisfactory result, so we revert and (2) perform a mirroring operation to arrive at our resulting composition. The semantic map to the right illustrates our path through semantic space.

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What are Table Cartograms Good for Anyway? An Algebraic Analysis

Cited by 16 publications

References 68 publications

No Grammar to Rule Them All: A Survey of JSON-style DSLs for Visualization

No Grammar to Rule Them All: A Survey of JSON-style DSLs for Visualization

The Transform-and-Perform Framework: Explainable Deep Learning Beyond Classification

Semantic Snapping for Guided Multi-View Visualization Design

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