Ryan Russell

dblp:170/1599 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2016
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Graphics, computer vision, multimedia, augmented reality and games · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer graphics and multimedia
1 paper
Visualization and visual analytics · 100%
Human-computer interaction and pervasive computing
1 paper
User interface design and tools · 100%
Software engineering, system software, and programming languages
1 paper
Compilers and program optimization · 100%

Topics — the 3 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
User interface design and tools
interaction design
0.212016
Reactive Vega: A Streaming Dataflow Architecture for Declarative Interactive Visualization · IEEE Trans. Vis. Comput. Graph. 2016
Compilers and program optimization › intermediate representation
dataflow graph
0.112016
Reactive Vega: A Streaming Dataflow Architecture for Declarative Interactive Visualization · IEEE Trans. Vis. Comput. Graph. 2016
Compilers and program optimization
dynamic optimization
0.112016
Reactive Vega: A Streaming Dataflow Architecture for Declarative Interactive Visualization · IEEE Trans. Vis. Comput. Graph. 2016

Methods — techniques the papers use, named apart from their topics

runtime optimization · 0.8compile-time optimization · 0.8benchmark study · 0.8data flow graph · 0.5dataflow graph · 0.2
YearPublicationVenuePosition
2016 Reactive Vega: A Streaming Dataflow Architecture for Declarative Interactive Visualization
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.
Arvind Satyanarayan, Ryan Russell, Jane Hoffswell, Jeffrey Heer
IEEE Trans. Vis. Comput. Graph.2