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49
Ensemble-Vis: A Framework for the Statistical Visualization of Ensemble Data
"... Abstract—Scientists increasingly use ensemble data sets to explore relationships present in dynamic systems. Ensemble data sets combine spatio-temporal simulation results generated using multiple numerical models, sampled input conditions and perturbed parameters. While ensemble data sets are a powe ..."
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Cited by 36 (3 self)
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Abstract—Scientists increasingly use ensemble data sets to explore relationships present in dynamic systems. Ensemble data sets combine spatio-temporal simulation results generated using multiple numerical models, sampled input conditions and perturbed parameters. While ensemble data sets are a powerful tool for mitigating uncertainty, they pose significant visualization and analysis challenges due to their complexity. In this article, we present Ensemble-Vis, a framework consisting of a collection of overview and statistical displays linked through a high level of interactivity. Ensemble-Vis allows scientists to gain key scientific insight into the distribution of simulation results as well as the uncertainty associated with the scientific data. In contrast to methods that present large amounts of diverse information in a single display, we argue that combining multiple linked displays yields a clearer presentation of the data and facilitates a greater level of visual data analysis. We demonstrate our framework using driving problems from climate modeling and meteorology and discuss generalizations to other fields. Index Terms—Ensemble data, uncertainty, statistical graphics, coordinated and linked views. I.
High Performance Multivariate Visual Data Exploration for Extremely Large Data
"... Abstract—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 sc ..."
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Cited by 35 (12 self)
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Abstract—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. I.
FastBit: Interactively Searching Massive Data
- PROC. OF SCIDAC 2009
, 2009
"... 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 techniques, ..."
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Cited by 20 (14 self)
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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 techniques, namely bitmap compression, encoding and binning. The advances in these techniques have led to a search tool that can 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.
Visualizing Temporal Patterns in Large Multivariate Data using Textual Pattern Matching
"... Abstract — Extracting and visualizing temporal patterns in large scientific data is an open problem in visualization research. First, there are few proven methods to flexibly and concisely define general temporal patterns for visualization. Second, with large timedependent data sets, as typical with ..."
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Cited by 19 (6 self)
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Abstract — Extracting and visualizing temporal patterns in large scientific data is an open problem in visualization research. First, there are few proven methods to flexibly and concisely define general temporal patterns for visualization. Second, with large timedependent data sets, as typical with today’s large-scale simulations, scalable and general solutions for handling the data are still not widely available. In this work, we have developed a textual pattern matching approach for specifying and identifying general temporal patterns. Besides defining the formalism of the language, we also provide a working implementation with sufficient efficiency and scalability to handle large data sets. Using recent large-scale simulation data from multiple application domains, we demonstrate that our visualization approach is one of the first to empower a concept driven exploration of large-scale time-varying multivariate data. Index Terms—Multivariate visualization, Time-varying, Uncertainty. 1
HDF5-FastQuery: Accelerating complex queries on HDF datasets using fast bitmap indices
- In SSDBM
, 2006
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Scalable Data Servers for Large Multivariate Volume Visualization
, 2006
"... Volumetric datasets with multiple variables on each voxel over multiple time steps are often complex, especially when considering the exponentially large attribute space formed by the variables in combination with the spatial and temporal dimensions. It is intuitive, practical, and thus often desira ..."
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Cited by 17 (9 self)
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Volumetric datasets with multiple variables on each voxel over multiple time steps are often complex, especially when considering the exponentially large attribute space formed by the variables in combination with the spatial and temporal dimensions. It is intuitive, practical, and thus often desirable, to interactively select a subset of the data from within that high-dimensional value space for efficient visualization. This approach is straightforward to implement if the dataset is small enough to be stored entirely in-core. However, to handle datasets sized at hundreds of gigabytes and beyond, this simplistic approach becomes infeasible and thus, more sophisticated solutions are needed. In this work, we developed a system that supports efficient visualization of an arbitrary subset, selected by range-queries, of a large multivariate time-varying dataset. By employing specialized data structures and schemes of data distribution, our system can leverage a large number of networked computers as parallel data servers, and guarantees a near optimal load-balance. We demonstrate our system of scalable data servers using two large time-varying simulation datasets.
R.: Terascale data organization for discovering multivariate climatic trends
- In Proceedings of the Conference on High Performance Computing Networking, Storage and Analysis (New
, 2009
"... Current visualization tools lack the ability to perform full-range spatial and temporal analysis on terascale scientific datasets. Two key reasons exist for this shortcoming: I/O and postprocessing on these datasets are being performed in suboptimal manners, and the subsequent data extraction and an ..."
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Cited by 15 (8 self)
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Current visualization tools lack the ability to perform full-range spatial and temporal analysis on terascale scientific datasets. Two key reasons exist for this shortcoming: I/O and postprocessing on these datasets are being performed in suboptimal manners, and the subsequent data extraction and analysis routines have not been studied in depth at large scales. We resolved these issues through advanced I/O tech-niques and improvements to current query-driven visualiza-tion methods. We show the efficiency of our approach by analyzing over a terabyte of multivariate satellite data and addressing two key issues in climate science: time-lag anal-ysis and drought assessment. Our methods allowed us to reduce the end-to-end execution times on these problems to one minute on a Cray XT4 machine.
Interactive exploration and analysis of large scale turbulent combustion using topology-based data segmentation
- IEEE Transactions on Visualization and Computer Graphics
, 2011
"... Abstract—Large-scale simulations are increasingly being used to study complex scientific and engineering phenomena. As a result, advanced visualization and data analysis are also becoming an integral part of the scientific process. Often, a key step in extracting insight from these large simulations ..."
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Cited by 12 (7 self)
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Abstract—Large-scale simulations are increasingly being used to study complex scientific and engineering phenomena. As a result, advanced visualization and data analysis are also becoming an integral part of the scientific process. Often, a key step in extracting insight from these large simulations involves the definition, extraction, and evaluation of features in the space and time coordinates of the solution. However, in many applications these features involve a range of parameters and decisions that will affect the quality and direction of the analysis. Examples include particular level sets of a specific scalar field, or local inequalities between derived quantities. A critical step in the analysis is to understand how these arbitrary parameters/decisions impact the statistical properties of the features, since such a characterization will help to evaluate the conclusions of the analysis as a whole. We present a new topological framework that in a single pass extracts and encodes entire families of possible features definitions as well as their statistical properties. For each time step we construct a hierarchical merge tree a highly compact, yet flexible feature representation. While this data structure is more than two orders of magnitude smaller than the raw simulation data it allows us to extract a set of feature for any given parameter selection in a post-processing step. Furthermore, we augment the trees with additional attributes making it possible to gather a large number of useful global, local, as well as conditional statistic that would otherwise be extremely difficult to compile. We also use this representation to create tracking graphs that describe the temporal evolution of the features over time. Our system provides a linked-view interface to explore the time-evolution of the graph interactively alongside the segmentation, thus making it possible to perform extensive data analysis in a very efficient manner. We demonstrate our framework
Variable interactions in query-driven visualization
- Visualization and Computer Graphics, IEEE Transactions on
"... Abstract—Our ability to generate ever-larger, increasingly-complex data, has established the need for scalable methods that identify, and provide insight into, important variable trends and interactions. Query-driven methods are among the small subset of techniques that are able to address both larg ..."
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Cited by 12 (1 self)
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Abstract—Our ability to generate ever-larger, increasingly-complex data, has established the need for scalable methods that identify, and provide insight into, important variable trends and interactions. Query-driven methods are among the small subset of techniques that are able to address both large and highly complex datasets. This paper presents a new method that increases the utility of querydriven techniques by visually conveying statistical information about the trends that exist between variables in a query. In this method, correlation fields, created between pairs of variables, are used with the cumulative distribution functions of variables expressed in a user’s query. This integrated use of cumulative distribution functions and correlation fields visually reveals, with respect to the solution space of the query, statistically important interactions between any three variables, and allows for trends between these variables to be readily identified. We demonstrate our method by analyzing interactions between variables in two flame-front simulations. Index Terms—Multivariate Data, Query-Driven Visualization 1
Query-Driven Visualization of Time-Varying Adaptive Mesh Refinement Data
"... Abstract—The visualization and analysis of AMR-based simulations is integral to the process of obtaining new insight in scientific research. We present a new method for performing query-driven visualization and analysis on AMR data, with specific emphasis on time-varying AMR data. Our work introduce ..."
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Cited by 10 (2 self)
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Abstract—The visualization and analysis of AMR-based simulations is integral to the process of obtaining new insight in scientific research. We present a new method for performing query-driven visualization and analysis on AMR data, with specific emphasis on time-varying AMR data. Our work introduces a new method that directly addresses the dynamic spatial and temporal properties of AMR grids that challenge many existing visualization techniques. Further, we present the first implementation of query-driven visualization on the GPU that uses a GPU-based indexing structure to both answer queries and efficiently utilize GPU memory. We apply our method to two different science domains to demonstrate its broad applicability. Index Terms—AMR, Query-Driven Visualization, Multitemporal Visualization 1
