Literature DB >> 26390466

Reactive Vega: A Streaming Dataflow Architecture for Declarative Interactive Visualization.

Arvind Satyanarayan, Ryan Russell, Jane Hoffswell, Jeffrey Heer.   

Abstract

We present Reactive Vega, a system architecture that provides the first robust and comprehensive treatment of declarative visual and interaction design for data visualization. Starting from a single declarative specification, Reactive Vega constructs a dataflow graph in which input data, scene graph elements, and interaction events are all treated as first-class streaming data sources. To support expressive interactive visualizations that may involve time-varying scalar, relational, or hierarchical data, Reactive Vega's dataflow graph can dynamically re-write itself at runtime by extending or pruning branches in a data-driven fashion. We discuss both compile- and run-time optimizations applied within Reactive Vega, and share the results of benchmark studies that indicate superior interactive performance to both D3 and the original, non-reactive Vega system.

Entities:  

Year:  2015        PMID: 26390466     DOI: 10.1109/TVCG.2015.2467091

Source DB:  PubMed          Journal:  IEEE Trans Vis Comput Graph        ISSN: 1077-2626            Impact factor:   4.579


  5 in total

1.  SOCRAT Platform Design: A Web Architecture for Interactive Visual Analytics Applications.

Authors:  Alexandr A Kalinin; Selvam Palanimalai; Ivo D Dinov
Journal:  Proc 2nd Workshop Hum Loop Data Anal (2017)       Date:  2017-04

2.  FAIR and Interactive Data Graphics from a Scientific Knowledge Graph.

Authors:  Michael E Deagen; Jamie P McCusker; Tolulomo Fateye; Samuel Stouffer; L Cate Brinson; Deborah L McGuinness; Linda S Schadler
Journal:  Sci Data       Date:  2022-05-27       Impact factor: 8.501

3.  Visualizing 'omic feature rankings and log-ratios using Qurro.

Authors:  Marcus W Fedarko; Cameron Martino; James T Morton; Antonio González; Gibraan Rahman; Clarisse A Marotz; Jeremiah J Minich; Eric E Allen; Rob Knight
Journal:  NAR Genom Bioinform       Date:  2020-04-28

4.  Dashboard-style interactive plots for RNA-seq analysis are R Markdown ready with Glimma 2.0.

Authors:  Hasaru Kariyawasam; Shian Su; Oliver Voogd; Matthew E Ritchie; Charity W Law
Journal:  NAR Genom Bioinform       Date:  2021-12-22

5.  Novel machine learning models to predict endocrine disruption activity for high-throughput chemical screening.

Authors:  Sean P Collins; Tara S Barton-Maclaren
Journal:  Front Toxicol       Date:  2022-09-20
  5 in total

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