Periscope: A Framework for Visualizations of Multiresolution Spatiotemporal Data at Scale

The crux of this study is to support browser-based visualizations of spatiotemporally evolving phenomena. Such phenomena arise in myriad domains spanning terrestrial, oceanic, and atmospheric processes. The data are voluminous, have diverse representational formats and projection systems, and are multivariate. We rely on a novel mix of tiling, caching, compression, perceptual limits, speculative prefetching, and dynamic generation of tiles. Our refinements at the client and server-side work in concert with each other to leverage client-side resources, minimize duplicate processing, and effective prefetching to ensure interactive explorations at scale. Our benchmarks profiled several aspects of our methodology and demonstrate the suitability of our refinements.

Authors

Institutions

Publication Details

Published
2024-12-15
DOI
https://doi.org/10.1109/bigdata62323.2024.10825317
Primary Topic
Data Visualization and Analytics
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Periscope: A Framework for Visualizations of Multiresolution Spatiotemporal Data at Scale

Paahuni Khandelwal, Sangmi Lee Pallickara, Matthew D. Young, Shrideep Pallickara et al.
Data Visualization and Analytics
article

Periscope: A Framework for Visualizations of Multiresolution Spatiotemporal Data at Scale

Paahuni Khandelwal, Sangmi Lee Pallickara, Matthew D. Young, Shrideep Pallickara, Everett Lewark
article en

Abstract

The crux of this study is to support browser-based visualizations of spatiotemporally evolving phenomena. Such phenomena arise in myriad domains spanning terrestrial, oceanic, and atmospheric processes. The data are voluminous, have diverse representational formats and projection systems, and are multivariate. We rely on a novel mix of tiling, caching, compression, perceptual limits, speculative prefetching, and dynamic generation of tiles. Our refinements at the client and server-side work in concert with each other to leverage client-side resources, minimize duplicate processing, and effective prefetching to ensure interactive explorations at scale. Our benchmarks profiled several aspects of our methodology and demonstrate the suitability of our refinements.

Colorado State University (US)
National Science Foundation
Openalex Percentile: Top 74%
Data Visualization and Analytics
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.