Demo Data from VLDB 2026 / reviewers in the wild / expert
W. Michael Wood-Vassey
dblp:134/8380
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2014
—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 · 88% Virtual and augmented reality · 12% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Visualization and visual analytics › geospatial visualization
big spatial data visualization |
0.2 | 1 | 2014 | Large-Scale Overlays and Trends: Visually Mining, Panning and Zoomingthe Observable Universe · IEEE Trans. Vis. Comput. Graph. 2014 |
Visualization and visual analytics › visual analytics › exploratory data analysis
visual data mining |
0.2 | 1 | 2014 | Large-Scale Overlays and Trends: Visually Mining, Panning and Zoomingthe Observable Universe · IEEE Trans. Vis. Comput. Graph. 2014 |
Virtual and augmented reality › navigation
interactive navigation |
0.1 | 1 | 2014 | Large-Scale Overlays and Trends: Visually Mining, Panning and Zoomingthe Observable Universe · IEEE Trans. Vis. Comput. Graph. 2014 |
Visualization and visual analytics › temporal data visualization
trend visualization |
0.1 | 1 | 2014 | Large-Scale Overlays and Trends: Visually Mining, Panning and Zoomingthe Observable Universe · IEEE Trans. Vis. Comput. Graph. 2014 |
Methods — techniques the papers use, named apart from their topics
spatial indexing · 0.2hardware-accelerated rendering · 0.2WebGL · 0.2HTML5 · 0.2
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2014 | Large-Scale Overlays and Trends: Visually Mining, Panning and Zoomingthe Observable UniverseabstractWe introduce a web-based computing infrastructure to assist the visual integration, mining and interactive navigation of large-scale astronomy observations. Following an analysis of the application domain, we design a client-server architecture to fetch distributed image data and to partition local data into a spatial index structure that allows prefix-matching of spatial objects. In conjunction with hardware-accelerated pixel-based overlays and an online cross-registration pipeline, this approach allows the fetching, displaying, panning and zooming of gigabit panoramas of the sky in real time. To further facilitate the integration and mining of spatial and non-spatial data, we introduce interactive trend images-compact visual representations for identifying outlier objects and for studying trends within large collections of spatial objects of a given class. In a demonstration, images from three sky surveys (SDSS, FIRST and simulated LSST results) are cross-registered and integrated as overlays, allowing cross-spectrum analysis of astronomy observations. Trend images are interactively generated from catalog data and used to visually mine astronomy observations of similar type. The front-end of the infrastructure uses the web technologies WebGL and HTML5 to enable cross-platform, web-based functionality. Our approach attains interactive rendering framerates; its power and flexibility enables it to serve the needs of the astronomy community. Evaluation on three case studies, as well as feedback from domain experts emphasize the benefits of this visual approach to the observational astronomy field; and its potential benefits to large scale geospatial visualization in general. Timothy Luciani, Brian A. Cherinka, Daniel Q. Oliphant, Sean Myers, W. Michael Wood-Vassey, Alexandros Labrinidis, G. Elisabeta Marai |
IEEE Trans. Vis. Comput. Graph. | 5 |