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ieee 5th symposium on large data analysis and visualization (LDAV)

作者： Widanagamaachchi, Wathsala Klacansky, Pavol Kolla, Hemanth Bhagatwala, Ankit Chen, Jackie Pascucci, Valerio Bremer, Peer-Timo Univ Utah SCI Inst Salt Lake City UT 84112 USA Sandia Natl Labs Albuquerque NM USA Lawrence Livermore Natl Lab Livermore CA USA

ISBN: (纸本)9781467385176

Understanding the temporal evolution of features of interest requires the ability to: (i) extract features from each snapshot;(ii) correlate them over time;and (iii) understand the resulting tracking graph. This paper provides new solutions for the last two challenges in the context of large-scale turbulent combustion simulations. In particular, we present a simple and general algorithm to correlate hierarchical features, embedded in time-dependent surfaces. This, for the first time, provides a parameter independent approach to track embedded features. Furthermore, we provide a new technique to adaptively change feature parameters over time to both: alleviate artifacts due to insufficient temporal resolution as well as to simplify the resulting tracking graphs to promote new scientific insights. Our solutions are integrated into a general and flexible analysis environment that allows users to interactively explore the spatio-temporal behavior of large-scale simulations. We demonstrate the results using the analysis of extinction holes in turbulent combustion as primary case study and a number of other applications to illustrate the generality of the approach.

关键词： E.1 [data STRUCTURES]: Graphs and Networks J.2 [PHYSICAL SCIENCES AND ENGINEERING]: Engineering turbulent combustion Extinction EXTINCTION temporal resolution embedding tracking

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Managing uncertainty in visualization and analysis of medical data

Managing uncertainty in visualization and analysis of medica...

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5th ieee International symposium on Biomedical Imaging

作者： Kniss, Joe Michael Univ New Mexico Albuquerque NM 87131 USA

ISBN: (纸本)9781424420025

The principal goal of visualization is to create a visual representation of complex information and large datasets in order to gain insight and understanding. Our current research focuses on methods for handling uncertainty stemming from data acquisition and algorithmic sources. Most visualization methods, especially those applied to 3D data, implicitly use some form of classification or segmentation to eliminate unimportant regions and illuminate those of interest. The process of classification is inherently uncertain;in many cases the source data contains error and noise, data transformations such as filtering can further introduce and magnify the uncertainty. More advanced classification methods rely on some sort of model or statistical method to determine what is and is not a feature of interest. While these classification methods can model uncertainty or fuzzy probabilistic memberships, they typically only provide discrete, maximum a-posteriori memberships. It is vital that visualization methods provide the user access to uncertainty in classification or image generation if the results of the visualization are to be trusted.

关键词： scientific visualization uncertainty sensitivity data processing computer graphics

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Parallel sets: Visual analysis of categorical data

Parallel sets: Visual analysis of categorical data

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ieee symposium on Information visualization (InfoVis 05)

作者： Bendix, F Kosara, R Hauser, H VRVis Research Center Austria Inte:Ligand

ISBN: (纸本)078039464X

The discrete nature of categorical data makes it a particular challenge for visualization. Methods that work very well for continuous data are often hardly usable with categorical dimensions. Only few methods deal properly with such data, mostly because of the discrete nature of categorical data, which does not translate well into the continuous domains of space and color. Parallel Sets is a new visualization method that adopts the layout of parallel coordinates, but substitutes the individual data points by a frequency-based representation. This abstracted view, combined with a set of carefully designed interactions, supports visual data analysis of large and complex data sets. The technique allows efficient work with meta data, which is particularly important when dealing with categorical datasets. By creating new dimensions from existing ones, for example, the user can filter the data according to his or her current needs. We also present the results from an interactive analysis of CRM data using Parallel Sets. We demonstrate how the flexible layout eases the process of knowledge crystallization, especially when combined with a sophisticated interaction scheme.

关键词： categorical data meta information interaction

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Towards a scalable and reliable real time in-network data analysis infrastructure

Towards a scalable and reliable real time in-network data an...

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1st ieee symposium on large-Scale data analysis and visualization 2011, LDAV 2011

作者： Ciraci, Selim Yin, Jian Pacific Northwest National Laboratory Richland WA United States

ISBN: (纸本)9781467301541

The smart grid applications requires real time analysis, response within the order of milliseconds and high-reliability because of the mission critical structure of the power grid system. The only way to satisfy these requirements is in-network data analysis and built-in redundancy routing for failures. To achieve this, we propose a data dissemination system that builds routes using network flow algorithms, have in network processing of the data and utilize data encoding to cope with high latencies. © 2011 ieee.

关键词： Electric power transmission networks

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DNS Firewall data visualization

DNS Firewall Data Visualization

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IFIP/ieee symposium on Integrated Network and Service Management (IM)

作者： Spacek, Stanislav Rusnak, Vit Dombajova, Anna-Marie Masaryk Univ Inst Comp Sci Brno Czech Republic Masaryk Univ Fac Informat Brno Czech Republic

ISBN: (纸本)9783903176157

Common security tools generate a lot of data suitable for further analysis. However, the raw form of the data is often too complex and useful information gets lost in a large volume of records. In this paper, we propose a system for visualization of the data generated by a DNS firewall and outline a process of visually emphasizing information important to incident handlers. Our prototype suggests that such visualization is possible, keeping the balance between the amount of displayed information and the level of detail.

关键词： data visualization

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Scalable multivariate volume visualization and analysis

Scalable multivariate volume visualization and analysis

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1st ieee symposium on large-Scale data analysis and visualization 2011, LDAV 2011

作者： Guo, Hanqi Xiao, He Lu, Min Yuan, Xiaoru School of EECS Peking University Beijing China Center for Computational Science and Engineering Peking University Beijing China

ISBN: (纸本)9781467301541

In this work, we present an effective and scalable system for multivariate volume data visualization and analysis with a novel Transfer Function (TF) interface design that tightly couples parallel coordinates plots (PCP) and MDS-based dimension projection plots. In our system, the PCP visualizes the data distribution of each variate and the MDS plots project features. Together, they are integrated seamlessly to provide flexible feature classification without context switching between different data presentations during the user interaction. The proposed interface enables users to identify relevant correlation clusters and assign optical properties on them. To further support large scale multivariate volume data visualization and analysis, we develop three integrated parallel systems to accelerate the rendering of PCP, the layout of MDS, as well as parallel rendering of multivarite volume data. Our experiments show that the system is effective in multivariate volume data visualization and its performance is scalable for data sets with different sizes and number of variates. © 2011 ieee.

关键词： Transfer functions

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Matches, Mismatches, and Methods: Multiple-View Workflows for Energy Portfolio analysis

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ieee TRANSACTIONS ON visualization AND COMPUTER GRAPHICS 2016年第1期22卷 449-458页

作者： Brehmer, Matthew Ng, Jocelyn Tate, Kevin Munzner, Tamara Univ British Columbia Vancouver BC V5Z 1M9 Canada EnerNOC Inc Boston MA USA

The energy performance of large building portfolios is challenging to analyze and monitor, as current analysis tools are not scalable or they present derived and aggregated data at too coarse of a level. We conducted a visualization design study, beginning with a thorough work domain analysis and a characterization of data and task abstractions. We describe generalizable visual encoding design choices for time-oriented data framed in terms of matches and mismatches, as well as considerations for workflow design. Our designs address several research questions pertaining to scalability, view coordination, and the inappropriateness of line charts for derived and aggregated data due to a combination of data semantics and domain convention. We also present guidelines relating to familiarity and trust, as well as methodological considerations for visualization design studies. Our designs were adopted by our collaborators and incorporated into the design of an energy analysis software application that will be deployed to tens of thousands of energy workers in their client base.

关键词： Design study design methodologies time series data task and requirements analysis coordinated and multiple views

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Social-aware collaborative visualization for large scientific projects

Social-aware collaborative visualization for large scientifi...

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International symposium on Collaborative Technologies and Systems

作者： Ma, Kwan-Liu Wang, Chaoli Univ Calif Davis Dept Comp Sci Davis CA 95616 USA

ISBN: (纸本)9781424422487

This paper discusses how to better support collaborative work for large scientific projects using visualization. Particular considerations are given to knowledge sharing and the social aspect of collaboration. The goal is to provide a roadmap for creating next-generation collaborative technologies with visual and social augmentation.

关键词： collaborative work information visualization social data analysis scientific visualization user interface design collective analysis

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Trelliscope: A system for detailed visualization in the deep analysis of large complex data

Trelliscope: A system for detailed visualization in the deep...

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2013 3rd ieee symposium on large-Scale data analysis and visualization, LDAV 2013

作者： Hafen, Ryan Gosink, Luke McDermott, Jason Rodland, Karin Dam, Kerstin Kleese-Van Cleveland, William S. Pacific Northwest National Lab United States Purdue University United States

ISBN: (纸本)9781479916597

Trelliscope emanates from the Trellis Display framework for visualization and the Divide and Recombine (D&R) approach to analyzing large complex data. In Trellis, the data are broken up into subsets, a visualization method is applied to each subset, and the display result is an array of panels, one per subset. This is a powerful framework for visualization of data, both small and large. In D&R, the data are broken up into subsets, and any analytic method from statistics and machine learning is applied to each subset independently. Then the outputs are recombined. This provides not only a powerful framework for analysis, but also feasible and practical computations using distributed computational facilities. It enables deep analysis of the data: study of both data summaries as well as the detailed data at their finest granularity. This is critical to full understanding of the data. It also enables the analyst to program using an interactive high-level language for data analysis such as R, which allows the analyst to focus more on the data and less on code. In this paper we introduce Trelliscope, a system that scales Trellis to large complex data. It provides a way to create displays with a very large number of panels and an interactive viewer that allows the analyst to sort, filter, and sample the panels in a meaningful way. We discuss the underlying principles, design, and scalable architecture of Trelliscope, and illustrate its use on three analysis projects in proteomics, high intensity physics, and power systems engineering. © 2013 ieee.

关键词： visualization

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Out-of-Core Dimensionality Reduction for large data via Out-of-Sample Extensions 14

Out-of-Core Dimensionality Reduction for Large Data via Out-...

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14th symposium on large data analysis and visualization

作者： Reichmann, Luca Haegele, David Weiskopf, Daniel Univ Stuttgart Stuttgart Germany

ISBN: (纸本)9798331516932;9798331516925

Dimensionality reduction (DR) is a well-established approach for the visualization of high-dimensional data sets. While DR methods are often applied to typical DR benchmark data sets in the literature, they might suffer from high runtime complexity and memory requirements, making them unsuitable for large data visualization especially in environments outside of high-performance computing. To perform DR on large data sets, we propose the use of out-of-sample extensions. Such extensions allow inserting new data into existing projections, which we leverage to iteratively project data into a reference projection that consists only of a small manageable subset. This process makes it possible to perform DR out-of-core on large data, which would otherwise not be possible due to memory and runtime limitations. For metric multidimensional scaling (MDS), we contribute an implementation with out-of-sample projection capability since typical software libraries do not support it. We provide an evaluation of the projection quality of five common DR algorithms (MDS, PCA, t-SNE, UMAP, and autoencoders) using quality metrics from the literature and analyze the trade-off between the size of the reference set and projection quality. The runtime behavior of the algorithms is also quantified with respect to reference set size, out-of-sample batch size, and dimensionality of the data sets. Furthermore, we compare the out-of-sample approach to other recently introduced DR methods, such as PaCMAP and TriMAP, which claim to handle larger data sets than traditional approaches. To showcase the usefulness of DR on this large scale, we contribute a use case where we analyze ensembles of streamlines amounting to one billion projected instances.

关键词： Dimensionality reduction out-of-core out-of-sample evaluation

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