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Modern Scientific Visualization is more than Just Pretty Pictures

Description: While the primary product of scientific visualization is images and movies, its primary objective is really scientific insight. Too often, the focus of visualization research is on the product, not the mission. This paper presents two case studies, both that appear in previous publications, that focus on using visualization technology to produce insight. The first applies"Query-Driven Visualization" concepts to laser wakefield simulation data to help identify and analyze the process of beam formation. The second uses topological analysis to provide a quantitative basis for (i) understanding the mixing process in hydrodynamic simulations, and (ii) performing comparative analysis of data from two different types of simulations that model hydrodynamic instability.
Date: December 5, 2008
Creator: Bethel, E Wes; Rubel, Oliver; Wu, Kesheng; Weber, Gunther; Pascucci, Valerio; Childs, Hank et al.
Partner: UNT Libraries Government Documents Department

Visualization and Analysis of 3D Gene Expression Data

Description: Recent methods for extracting precise measurements ofspatial gene expression patterns from three-dimensional (3D) image dataopens the way for new analysis of the complex gene regulatory networkscontrolling animal development. To support analysis of this novel andhighly complex data we developed PointCloudXplore (PCX), an integratedvisualization framework that supports dedicated multi-modal, physical andinformation visualization views along with algorithms to aid in analyzingthe relationships between gene expression levels. Using PCX, we helpedour science stakeholders to address many questions in 3D gene expressionresearch, e.g., to objectively define spatial pattern boundaries andtemporal profiles of genes and to analyze how mRNA patterns arecontrolled by their regulatory transcription factors.
Date: October 25, 2007
Creator: Bethel, E. Wes; Rubel, Oliver; Weber, Gunther H.; Hamann, Bernd & Hagen, Hans
Partner: UNT Libraries Government Documents Department

Recent Advances in VisIt: AMR Streamlines and Query-Driven Visualization

Description: Adaptive Mesh Refinement (AMR) is a highly effective method for simulations spanning a large range of spatiotemporal scales such as those encountered in astrophysical simulations. Combining research in novel AMR visualization algorithms and basic infrastructure work, the Department of Energy's (DOEs) Science Discovery through Advanced Computing (SciDAC) Visualization and Analytics Center for Enabling Technologies (VACET) has extended VisIt, an open source visualization tool that can handle AMR data without converting it to alternate representations. This paper focuses on two recent advances in the development of VisIt. First, we have developed streamline computation methods that properly handle multi-domain data sets and utilize effectively multiple processors on parallel machines. Furthermore, we are working on streamline calculation methods that consider an AMR hierarchy and detect transitions from a lower resolution patch into a finer patch and improve interpolation at level boundaries. Second, we focus on visualization of large-scale particle data sets. By integrating the DOE Scientific Data Management (SDM) Center's FastBit indexing technology into VisIt, we are able to reduce particle counts effectively by thresholding and by loading only those particles from disk that satisfy the thresholding criteria. Furthermore, using FastBit it becomes possible to compute parallel coordinate views efficiently, thus facilitating interactive data exploration of massive particle data sets.
Date: November 12, 2009
Creator: Weber, Gunther; Ahern, Sean; Bethel, Wes; Borovikov, Sergey; Childs, Hank; Deines, Eduard et al.
Partner: UNT Libraries Government Documents Department

Automated analysis for detecting beams in laser wakefield simulations

Description: Laser wakefield particle accelerators have shown the potential to generate electric fields thousands of times higher than those of conventional accelerators. The resulting extremely short particle acceleration distance could yield a potential new compact source of energetic electrons and radiation, with wide applications from medicine to physics. Physicists investigate laser-plasma internal dynamics by running particle-in-cell simulations; however, this generates a large dataset that requires time-consuming, manual inspection by experts in order to detect key features such as beam formation. This paper describes a framework to automate the data analysis and classification of simulation data. First, we propose a new method to identify locations with high density of particles in the space-time domain, based on maximum extremum point detection on the particle distribution. We analyze high density electron regions using a lifetime diagram by organizing and pruning the maximum extrema as nodes in a minimum spanning tree. Second, we partition the multivariate data using fuzzy clustering to detect time steps in a experiment that may contain a high quality electron beam. Finally, we combine results from fuzzy clustering and bunch lifetime analysis to estimate spatially confined beams. We demonstrate our algorithms successfully on four different simulation datasets.
Date: July 3, 2008
Creator: Ushizima, Daniela M.; Rubel, Oliver; Prabhat, Mr.; Weber, Gunther H.; Bethel, E. Wes; Aragon, Cecilia R. et al.
Partner: UNT Libraries Government Documents Department

High Performance Multivariate Visual Data Exploration for Extremely Large Data

Description: One of the central challenges in modern science is the need to quickly derive knowledge and understanding from large, complex collections of data. We present a new approach that deals with this challenge by combining and extending techniques from high performance visual data analysis and scientific data management. This approach is demonstrated within the context of gaining insight from complex, time-varying datasets produced by a laser wakefield accelerator simulation. Our approach leverages histogram-based parallel coordinates for both visual information display as well as a vehicle for guiding a data mining operation. Data extraction and subsetting are implemented with state-of-the-art index/query technology. This approach, while applied here to accelerator science, is generally applicable to a broad set of science applications, and is implemented in a production-quality visual data analysis infrastructure. We conduct a detailed performance analysis and demonstrate good scalability on a distributed memory Cray XT4 system.
Date: August 22, 2008
Creator: Rubel, Oliver; Wu, Kesheng; Childs, Hank; Meredith, Jeremy; Geddes, Cameron G.R.; Cormier-Michel, Estelle et al.
Partner: UNT Libraries Government Documents Department

Application of High-performance Visual Analysis Methods to Laser Wakefield Particle Acceleration Data

Description: Our work combines and extends techniques from high-performance scientific data management and visualization to enable scientific researchers to gain insight from extremely large, complex, time-varying laser wakefield particle accelerator simulation data. We extend histogram-based parallel coordinates for use in visual information display as well as an interface for guiding and performing data mining operations, which are based upon multi-dimensional and temporal thresholding and data subsetting operations. To achieve very high performance on parallel computing platforms, we leverage FastBit, a state-of-the-art index/query technology, to accelerate data mining and multi-dimensional histogram computation. We show how these techniques are used in practice by scientific researchers to identify, visualize and analyze a particle beam in a large, time-varying dataset.
Date: August 28, 2008
Creator: Rubel, Oliver; Prabhat, Mr.; Wu, Kesheng; Childs, Hank; Meredith, Jeremy; Geddes, Cameron G.R. et al.
Partner: UNT Libraries Government Documents Department

FastBit: Interactively Searching Massive Data

Description: As scientific instruments and computer simulations produce more and more data, the task of locating the essential information to gain insight becomes increasingly difficult. FastBit is an efficient software tool to address this challenge. In this article, we present a summary of the key underlying technologies, namely bitmap compression, encoding, and binning. Together these techniques enable FastBit to answer structured (SQL) queries orders of magnitude faster than popular database systems. To illustrate how FastBit is used in applications, we present three examples involving a high-energy physics experiment, a combustion simulation, and an accelerator simulation. In each case, FastBit significantly reduces the response time and enables interactive exploration on terabytes of data.
Date: June 23, 2009
Creator: Wu, Kesheng; Ahern, Sean; Bethel, E. Wes; Chen, Jacqueline; Childs, Hank; Cormier-Michel, Estelle et al.
Partner: UNT Libraries Government Documents Department

Laser Plasma Particle Accelerators: Large Fields for Smaller Facility Sources

Description: Compared to conventional particle accelerators, plasmas can sustain accelerating fields that are thousands of times higher. To exploit this ability, massively parallel SciDAC particle simulations provide physical insight into the development of next-generation accelerators that use laser-driven plasma waves. These plasma-based accelerators offer a path to more compact, ultra-fast particle and radiation sources for probing the subatomic world, for studying new materials and new technologies, and for medical applications.
Date: March 20, 2009
Creator: Geddes, Cameron G.R.; Cormier-Michel, Estelle; Esarey, Eric H.; Schroeder, Carl B.; Vay, Jean-Luc; Leemans, Wim P. et al.
Partner: UNT Libraries Government Documents Department

Coupling Visualization and Data Analysis for Knowledge Discovery from Multi-dimensional Scientific Data

Description: Knowledge discovery from large and complex scientific data is a challenging task. With the ability to measure and simulate more processes at increasingly finer spatial and temporal scales, the growing number of data dimensions and data objects presents tremendous challenges for effective data analysis and data exploration methods and tools. The combination and close integration of methods from scientific visualization, information visualization, automated data analysis, and other enabling technologies"such as efficient data management" supports knowledge discovery from multi-dimensional scientific data. This paper surveys two distinct applications in developmental biology and accelerator physics, illustrating the effectiveness of the described approach.
Date: June 8, 2010
Creator: Rubel, Oliver; Ahern, Sean; Bethel, E. Wes; Biggin, Mark D.; Childs, Hank; Cormier-Michel, Estelle et al.
Partner: UNT Libraries Government Documents Department

PointCloudXplore: a visualization tool for 3D gene expressiondata

Description: The Berkeley Drosophila Transcription Network Project (BDTNP) has developed a suite of methods that support quantitative, computational analysis of three-dimensional (3D) gene expression patterns with cellular resolution in early Drosophila embryos, aiming at a more in-depth understanding of gene regulatory networks. We describe a new tool, called PointCloudXplore (PCX), that supports effective 3D gene expression data exploration. PCX is a visualization tool that uses the established visualization techniques of multiple views, brushing, and linking to support the analysis of high-dimensional datasets that describe many genes' expression. Each of the views in PointCloudXplore shows a different gene expression data property. Brushing is used to select and emphasize data associated with defined subsets of embryo cells within a view. Linking is used to show in additional views the expression data for a group of cells that have first been highlighted as a brush in a single view, allowing further data subset properties to be determined. In PCX, physical views of the data are linked to abstract data displays such as parallel coordinates. Physical views show the spatial relationships between different genes' expression patterns within an embryo. Abstract gene expression data displays on the other hand allow for an analysis of relationships between different genes directly in the gene expression space. We discuss on parallel coordinates as one example abstract data view currently available in PCX. We have developed several extensions to standard parallel coordinates to facilitate brushing and the visualization of 3D gene expression data.
Date: October 1, 2006
Creator: Rubel, Oliver; Weber, Gunther H.; Keranen, Soile V.E.; Fowlkes,Charles C.; Luengo Hendriks, Cristian L.; Simirenko, Lisa et al.
Partner: UNT Libraries Government Documents Department

DOE's SciDAC Visualization and Analytics Center for EnablingTechnologies -- Strategy for Petascale Visual Data Analysis Success

Description: The focus of this article is on how one group of researchersthe DOE SciDAC Visualization and Analytics Center for EnablingTechnologies (VACET) is tackling the daunting task of enabling knowledgediscovery through visualization and analytics on some of the world slargest and most complex datasets and on some of the world's largestcomputational platforms. As a Center for Enabling Technology, VACET smission is the creation of usable, production-quality visualization andknowledge discovery software infrastructure that runs on large, parallelcomputer systems at DOE's Open Computing facilities and that providessolutions to challenging visual data exploration and knowledge discoveryneeds of modern science, particularly the DOE sciencecommunity.
Date: October 1, 2007
Creator: Bethel, E. Wes; Johnson, Chris; Aragon, Cecilia; Prabhat, ???; Rubel, Oliver; Weber, Gunther et al.
Partner: UNT Libraries Government Documents Department

Occam's Razor and Petascale Visual Data Analysis

Description: One of the central challenges facing visualization research is how to effectively enable knowledge discovery. An effective approach will likely combine application architectures that are capable of running on today?s largest platforms to address the challenges posed by large data with visual data analysis techniques that help find, represent, and effectively convey scientifically interesting features and phenomena.
Date: June 12, 2009
Creator: Bethel, E. Wes; Johnson, Chris; Ahern, Sean; Bell, John; Bremer, Peer-Timo; Childs, Hank et al.
Partner: UNT Libraries Government Documents Department

Integrating Data Clustering and Visualization for the Analysis of 3D Gene Expression Data

Description: The recent development of methods for extracting precise measurements of spatial gene expression patterns from three-dimensional (3D) image data opens the way for new analyses of the complex gene regulatory networks controlling animal development. We present an integrated visualization and analysis framework that supports user-guided data clustering to aid exploration of these new complex datasets. The interplay of data visualization and clustering-based data classification leads to improved visualization and enables a more detailed analysis than previously possible. We discuss (i) integration of data clustering and visualization into one framework; (ii) application of data clustering to 3D gene expression data; (iii) evaluation of the number of clusters k in the context of 3D gene expression clustering; and (iv) improvement of overall analysis quality via dedicated post-processing of clustering results based on visualization. We discuss the use of this framework to objectively define spatial pattern boundaries and temporal profiles of genes and to analyze how mRNA patterns are controlled by their regulatory transcription factors.
Date: May 12, 2008
Creator: Data Analysis and Visualization (IDAV) and the Department of Computer Science, University of California, Davis, One Shields Avenue, Davis CA 95616, USA,; nternational Research Training Group ``Visualization of Large and Unstructured Data Sets,'' University of Kaiserslautern, Germany; Computational Research Division, Lawrence Berkeley National Laboratory, One Cyclotron Road, Berkeley, CA 94720, USA; Genomics Division, Lawrence Berkeley National Laboratory, One Cyclotron Road, Berkeley CA 94720, USA; Life Sciences Division, Lawrence Berkeley National Laboratory, One Cyclotron Road, Berkeley CA 94720, USA,; Computer Science Division,University of California, Berkeley, CA, USA, et al.
Partner: UNT Libraries Government Documents Department

PointCloudExplore 2: Visual exploration of 3D gene expression

Description: To better understand how developmental regulatory networks are defined inthe genome sequence, the Berkeley Drosophila Transcription Network Project (BDNTP)has developed a suite of methods to describe 3D gene expression data, i.e.,the output of the network at cellular resolution for multiple time points. To allow researchersto explore these novel data sets we have developed PointCloudXplore (PCX).In PCX we have linked physical and information visualization views via the concept ofbrushing (cell selection). For each view dedicated operations for performing selectionof cells are available. In PCX, all cell selections are stored in a central managementsystem. Cells selected in one view can in this way be highlighted in any view allowingfurther cell subset properties to be determined. Complex cell queries can be definedby combining different cell selections using logical operations such as AND, OR, andNOT. Here we are going to provide an overview of PointCloudXplore 2 (PCX2), thelatest publicly available version of PCX. PCX2 has shown to be an effective tool forvisual exploration of 3D gene expression data. We discuss (i) all views available inPCX2, (ii) different strategies to perform cell selection, (iii) the basic architecture ofPCX2., and (iv) illustrate the usefulness of PCX2 using selected examples.
Date: March 31, 2008
Creator: International Research Training Group Visualization of Large and Unstructured Data Sets, University of Kaiserslautern, Germany; Institute for Data Analysis and Visualization, University of California, Davis, CA; Computational Research Division, Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA; Genomics Division, LBNL; Computer Science Department, University of California, Irvine, CA; Computer Science Division,University of California, Berkeley, CA et al.
Partner: UNT Libraries Government Documents Department