Dataless Sharing of Interactive Visualization

Raji, Mohammad; Duncan, Jeremiah; Hobson, Tanner; Huang, Jian

doi:10.1109/tvcg.2020.2984708

Cited by 6 publications

(5 citation statements)

References 29 publications

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“…To serve the intended use as a means for analyzing or disseminating data, an interactive data graphic must retain its ability to respond to user input when embedded in some other format (e.g., offline document, presentation slide), or offer a pre-recorded animation displaying its contents. Two recent projects, Chameleon and Loom, have begun to tackle some of these challenges around portability of interactive data graphics 38 , 39 .…”

Section: Discussionmentioning

confidence: 99%

FAIR and Interactive Data Graphics from a Scientific Knowledge Graph

et al. 2022

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Graph databases capture richly linked domain knowledge by integrating heterogeneous data and metadata into a unified representation. Here, we present the use of bespoke, interactive data graphics (bar charts, scatter plots, etc.) for visual exploration of a knowledge graph. By modeling a chart as a set of metadata that describes semantic context (SPARQL query) separately from visual context (Vega-Lite specification), we leverage the high-level, declarative nature of the SPARQL and Vega-Lite grammars to concisely specify web-based, interactive data graphics synchronized to a knowledge graph. Resources with dereferenceable URIs (uniform resource identifiers) can employ the hyperlink encoding channel or image marks in Vega-Lite to amplify the information content of a given data graphic, and published charts populate a browsable gallery of the database. We discuss design considerations that arise in relation to portability, persistence, and performance. Altogether, this pairing of SPARQL and Vega-Lite—demonstrated here in the domain of polymer nanocomposite materials science—offers an extensible approach to FAIR (findable, accessible, interoperable, reusable) scientific data visualization within a knowledge graph framework.

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Section: Discussionmentioning

confidence: 99%

FAIR and Interactive Data Graphics from a Scientific Knowledge Graph

et al. 2022

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“…Two aspects of obtaining such data shall be researched: (1) the semantic abstractions or tags for describing interactivity and animation, and (2) the methods for capturing and understanding interactivity and animation. Some previous works have made efforts in these two aspects: Park et al [PMK08] and Myers et al [MPN * 08] examined how designers design and describe interactive behaviors, and Raji et al [RDHH20] developed a system called Loom to capture and share interactive visualization in the Tableau application. More research needs to be investigated to help record the interactivity and animation information in a corpus, which can further support research efforts in RO1 (Beyond Chart Types) and RO6 (Interactive and Animated Charts).…”

Section: Challenges and Opportunitiesmentioning

confidence: 99%

The State of the Art in Creating Visualization Corpora for Automated Chart Analysis

Chen

Liu

2023

Computer Graphics Forum

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We present a state‐of‐the‐art report on visualization corpora in automated chart analysis research. We survey 56 papers that created or used a visualization corpus as the input of their research techniques or systems. Based on a multi‐level task taxonomy that identifies the goal, method, and outputs of automated chart analysis, we examine the property space of existing chart corpora along five dimensions: format, scope, collection method, annotations, and diversity. Through the survey, we summarize common patterns and practices of creating chart corpora, identify research gaps and opportunities, and discuss the desired properties of future benchmark corpora and the required tools to create them.

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“…However, when rendering them as visualizations, extra compilers are always required, which limits their compatibility and wide usage. Recently, to combine the advantages of graphics and programs, several studies have also explored hybrid approaches by embedding programs into graphics, such as [12,32,56].…”

Section: Visualization Formatmentioning

confidence: 99%

Structure-aware Visualization Retrieval

Li,

Wang,

et al. 2022

Preprint

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With the wide usage of data visualizations, a huge number of Scalable Vector Graphic (SVG)-based visualizations have been created and shared online. Accordingly, there has been an increasing interest in exploring how to retrieve perceptually similar visualizations from a large corpus, since it can benefit various downstream applications such as visualization recommendation. Existing methods mainly focus on the visual appearance of visualizations by regarding them as bitmap images. However, the structural information intrinsically existing in SVG-based visualizations is ignored. Such structural information can delineate the spatial and hierarchical relationship among visual elements, and characterize visualizations thoroughly from a new perspective. This paper presents a structureaware method to advance the performance of visualization retrieval by collectively considering both the visual and structural information. We extensively evaluated our approach through quantitative comparisons, a user study and case studies. The results demonstrate the effectiveness of our approach and its advantages over existing methods.

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Dataless Sharing of Interactive Visualization

Cited by 6 publications

References 29 publications

FAIR and Interactive Data Graphics from a Scientific Knowledge Graph

FAIR and Interactive Data Graphics from a Scientific Knowledge Graph

The State of the Art in Creating Visualization Corpora for Automated Chart Analysis

Structure-aware Visualization Retrieval

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