ADR visualization: A generalized framework for ranking large-scale scientific data using Analysis-Driven Refinement

Prioritization of data is necessary for managing large-scale scientific data, as the scale of the data implies that there are only enough resources available to process a limited subset of the data. For example, data prioritization is used during in situ triage to scale with bandwidth bottlenecks, a...

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Vydané v:2014 IEEE 4th Symposium on Large Data Analysis and Visualization (LDAV) s. 43 - 50
Hlavní autori: Nouanesengsy, Boonthanome, Woodring, Jonathan, Patchett, John, Myers, Kary, Ahrens, James
Médium: Konferenčný príspevok..
Jazyk:English
Vydavateľské údaje: IEEE 01.11.2014
Predmet:
adaptive mesh refinement
big data
Context
Data models
data triage
Data visualization
Entropy
Extraterrestrial measurements
focus+context
large-scale data
Partitioning algorithms
prioritization
scientific data
Time measurement
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Shrnutí:Prioritization of data is necessary for managing large-scale scientific data, as the scale of the data implies that there are only enough resources available to process a limited subset of the data. For example, data prioritization is used during in situ triage to scale with bandwidth bottlenecks, and used during focus+context visualization to save time during analysis by guiding the user to important information. In this paper, we present ADR visualization, a generalized analysis framework for ranking large-scale data using Analysis-Driven Refinement (ADR), which is inspired by Adaptive Mesh Refinement (AMR). A large-scale data set is partitioned in space, time, and variable, using user-defined importance measurements for prioritization. This process creates a prioritization tree over the data set. Using this tree, selection methods can generate sparse data products for analysis, such as focus+context visualizations or sparse data sets.
DOI:10.1109/LDAV.2014.7013203