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Title: High Performance Visualization using Query-Driven Visualizationand Analytics

Abstract

Query-driven visualization and analytics is a unique approach for high-performance visualization that offers new capabilities for knowledge discovery and hypothesis testing. The new capabilities akin to finding needles in haystacks are the result of combining technologies from the fields of scientific visualization and scientific data management. This approach is crucial for rapid data analysis and visualization in the petascale regime. This article describes how query-driven visualization is applied to a hero-sized network traffic analysis problem.

Authors:
; ; ; ; ;
Publication Date:
Research Org.:
Ernest Orlando Lawrence Berkeley NationalLaboratory, Berkeley, CA (US)
Sponsoring Org.:
USDOE Director. Office of Science. Office of AdvancedScientific Computing Research
OSTI Identifier:
888965
Report Number(s):
LBNL/PUB-959
R&D Project: K11107; BnR: KJ0101030; TRN: US200619%%287
DOE Contract Number:
DE-AC02-05CH11231
Resource Type:
Technical Report
Country of Publication:
United States
Language:
English
Subject:
99 GENERAL AND MISCELLANEOUS//MATHEMATICS, COMPUTING, AND INFORMATION SCIENCE; DATA ANALYSIS; HYPOTHESIS; MANAGEMENT; PERFORMANCE; TESTING; high performance visualization network traffic analysis datamining

Citation Formats

Bethel, E. Wes, Campbell, Scott, Dart, Eli, Shalf, John, Stockinger, Kurt, and Wu, Kesheng. High Performance Visualization using Query-Driven Visualizationand Analytics. United States: N. p., 2006. Web. doi:10.2172/888965.
Bethel, E. Wes, Campbell, Scott, Dart, Eli, Shalf, John, Stockinger, Kurt, & Wu, Kesheng. High Performance Visualization using Query-Driven Visualizationand Analytics. United States. doi:10.2172/888965.
Bethel, E. Wes, Campbell, Scott, Dart, Eli, Shalf, John, Stockinger, Kurt, and Wu, Kesheng. 2006. "High Performance Visualization using Query-Driven Visualizationand Analytics". United States. doi:10.2172/888965. https://www.osti.gov/servlets/purl/888965.
@article{osti_888965,
title = {High Performance Visualization using Query-Driven Visualizationand Analytics},
author = {Bethel, E. Wes and Campbell, Scott and Dart, Eli and Shalf, John and Stockinger, Kurt and Wu, Kesheng},
abstractNote = {Query-driven visualization and analytics is a unique approach for high-performance visualization that offers new capabilities for knowledge discovery and hypothesis testing. The new capabilities akin to finding needles in haystacks are the result of combining technologies from the fields of scientific visualization and scientific data management. This approach is crucial for rapid data analysis and visualization in the petascale regime. This article describes how query-driven visualization is applied to a hero-sized network traffic analysis problem.},
doi = {10.2172/888965},
journal = {},
number = ,
volume = ,
place = {United States},
year = 2006,
month = 6
}

Technical Report:

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  • Triplestores that support query languages such as SPARQL are emerging as the preferred and scalable solution to represent data and meta-data as massive heterogeneous graphs using Semantic Web standards. With increasing adoption, the desire to conduct graph-theoretic mining and exploratory analysis has also increased. Addressing that desire, this paper presents a solution that is the marriage of Graph Theory and the Semantic Web. We present software that can analyze Linked Data using graph operations such as counting triangles, finding eccentricity, testing connectedness, and computing PageRank directly on triple stores via the SPARQL interface. We describe the process of optimizing performancemore » of the SPARQL-based implementation of such popular graph algorithms by reducing the space-overhead, simplifying iterative complexity and removing redundant computations by understanding query plans. Our optimized approach shows significant performance gains on triplestores hosted on stand-alone workstations as well as hardware-optimized scalable supercomputers such as the Cray XMT.« less
  • This document is the final report for a WFO agreement between LBNL and the National Visualization and Analytics Center at PNNL. The document lists project milestones, their completion date, research results and findings. In brief, the project focuses on testing the hypothesis that the duty cycle in scientific discovery can be reduced by combining visual analytics interfaces, novel visualization techniques and scientific data management technology.
  • One fundamental element of scientific inquiry is discoveringrelationships, particularly the interactions between different variablesin observed or simulated phenomena. Building upon our prior work in thefield of Query-Driven Visualization, where visual data analysisprocessing is focused on subsets of large data deemed to be"scientifically interesting," this new work focuses on a novel knowledgediscovery capability suitable for use with petascale class datasets. Itenables visual presentation of the presence or absence of relationships(correlations) between variables in data subsets produced by Query-Drivenmethodologies. This technique holds great potential for enablingknowledge discovery from large and complex datasets currently emergingfrom SciDAC and INCITE projects. It is sufficiently generallymore » to beapplicable to any time of complex, time-varying, multivariate data fromstructured, unstructured or adaptive grids.« less