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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Podrobná bibliografie
Vydáno v:	2014 IEEE 4th Symposium on Large Data Analysis and Visualization (LDAV) s. 43 - 50
Hlavní autoři:	Nouanesengsy, Boonthanome, Woodring, Jonathan, Patchett, John, Myers, Kary, Ahrens, James
Médium:	Konferenční příspěvek
Jazyk:	angličtina
Vydáno:	IEEE 01.11.2014
Témata:	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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Popis
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