D³ Data-Driven Documents

Bostock, M. G.; Ogievetsky, V.; Heer, Jeffrey

doi:10.1109/tvcg.2011.185

Cited by 2,574 publications

(1,615 citation statements)

References 18 publications

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“…HazMatMapper is built with D3 (Data-Driven Documents) (Bostock, Ogievetsky, & Heer, 2011;http:// www.d3js.org), an open source JavaScript library for information visualization and interactive mapping (Sack, Donohue, & Roth, 2014). D3 extends JavaScript to include native functions for accessing and manipulating data as users interact with it in browser.…”

Section: Softwarementioning

confidence: 99%

HazMatMapper: an online and interactive geographic visualization tool for exploring transnational flows of hazardous waste and environmental justice

et al. 2017

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Section: Softwarementioning

confidence: 99%

HazMatMapper: an online and interactive geographic visualization tool for exploring transnational flows of hazardous waste and environmental justice

et al. 2017

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“…Bostock et al proposed a JavaScript library, called Data-Driven Documents (D3), which serves as a flexible infrastructure for many types of visualizations. 2 Our system will provide tools built on the D3 library.…”

Section: Related Workmentioning

confidence: 99%

Parameter Space Visualization for Large-scale Datasets Using Parallel Coordinate Plots

Glendenning¹,

Wischgoll²,

Harris³

et al. 2016

jist

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Abstract. Visualization is an important task in data analytics, as it allows researchers to view patterns within the data instead of reading through extensive raw data. Allowing the ability to interact with the visualizations is an essential aspect, since it provides the ability to intuitively explore data to find meaning and patterns more efficiently. Interactivity, however, becomes progressively more difficult as the size of the dataset increases. This project begins by leveraging existing web-based data visualization technologies, and extends their functionality through the use of parallel processing. This methodology utilizes state-of-the-art techniques, such as Node.js, to split the visualization rendering and user interactivity controls between a client-server infrastructure without having to rebuild the visualization technologies. The approach minimizes data transfer by performing the rendering step on the server while allowing for the use of high-performance computing systems to render the visualizations more quickly. In order to improve the scaling of the system with larger datasets, parallel processing and visualization optimization techniques are used. This work uses parameter space data generated from mindmodeling.org to showcase the authors' methodology for handling large-scale datasets while retaining interactivity and user friendliness. INTRODUCTIONThe ability to make rapid visual assessments of parameter spaces has the potential to change the workflow for both model simulation and model fitting/parameter recovery. It enables the rapid identification of input parameters that result in similar output data or model behaviors. This allows researchers to eliminate redundant input parameters for more efficient use of modeling and simulation computational resources. For example, should two parameters exhibit a strong correlation, one might be held constant while the other is varied in order to capture all of the unique

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“…Client-side features have been developed using Hypertext Markup Language (HTML), Cascading Style Sheets (CSS), the Dojo toolkit for user interface widgets, and basic content elements. The Scalable Vector Graphics (SVG)-based library D3 50 is used for most of the visualization rendering, though some visualization components use a custom JavaScript library built on top of HTML5's Canvas element.…”

Section: Cava Platform Implementation Detailsmentioning

confidence: 99%

Iterative cohort analysis and exploration

Zhang

Gotz

Perer

2014

Information Visualization

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Cohort analysis is a widely used technique for the investigation of risk factors for groups of people. It is commonly employed to gain insights about interesting subsets of a population in fields such as medicine, bioinformatics, and social science. The nature of these analyses is evolving as larger collections of data about individuals become available. Examples of emerging large-scale data sources include electronic medical record systems and social network datasets. When domain experts perform cohort analyses using such massive datasets, they typically rely on a team of technologists to help manage and process the data. This results in a slow and cumbersome analysis process in which iterative exploration is difficult. To address this challenge, we are exploring technologies designed to help domain experts work more independently and more quickly. This article describes CAVA, a platform for Cohort Analysis via Visual Analytics. We introduce three primary types of artifacts (cohorts, views, and analytics) and an architecture that connects these elements together to provide an interactive exploratory analysis environment designed for domain experts. In addition to the CAVA design, this article presents two use cases from the health-care domain and a domain-expert evaluation to demonstrate the power of our approach.

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D³ Data-Driven Documents

Cited by 2,574 publications

References 18 publications

HazMatMapper: an online and interactive geographic visualization tool for exploring transnational flows of hazardous waste and environmental justice

HazMatMapper: an online and interactive geographic visualization tool for exploring transnational flows of hazardous waste and environmental justice

Parameter Space Visualization for Large-scale Datasets Using Parallel Coordinate Plots

Iterative cohort analysis and exploration

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