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13th ieee symposium on Embedded Systems for Real-Time Multimedia (ESTIMedia)

作者： Middendorf, Lars Haubelt, Christian Univ Rostock D-18055 Rostock Germany

ISBN: (纸本)9781467381642

Although modern graphics processing units (GPU) contain a large number of programmable shader cores, the focus on data parallelism and also the lack of efficient on-chip communication hinder the creation of custom graphics pipelines with arbitrary topologies. Based on the concept of stream rewriting, we propose a novel many-core architecture for graphics processing, which supports dynamic scheduling of recursively expandable task graphs and graphics pipelines. In particular, the tasks and their dependencies are encoded as a token stream, which is iteratively rewritten via pattern matching on multiple cores in parallel. The scalability of the proposed hardware architecture has been evaluated using an FPGA prototype.

关键词： Dynamic scheduling graphics graphics processing units Hardware Pipeline processing Pipelines graphic images dynamic scheduling Pipeline processing pipeline core construction Computer hardware graphics Processing Unit piping hardware architecture graphics processing task graphs data parallel streams

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Reducing latency to volume visualization on PC cluster

Reducing latency to volume visualization on PC cluster

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14th Brazilian symposium on Computer graphics and Image Processing

作者： Bressan, PA Zuffo, MK Univ Sao Paulo Polytech Sch Lab Integrated Syst EPUSP Sao Paulo Brazil

ISBN: (纸本)0769513301

Volume visualization is an important tool in many scientific applications, requiring intensive processing and dealing with large amounts of data. Therefore the size of these data always exceeds the processing and visualization capacities of the conventional workstations. In this technical poster we present our working in progress about techniques of data prefetching and data prediction to volume visualization on low cost PC clusters. Our approach is based on the Distributed Shared Memory (DSM) paradigm [3], which one can greatly facilitate the parallel programming efforts on volume visualization applications.

关键词： parallel volume visualization PC cluster DSM data prefetching data prediction

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Spatial analysis of news sources

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ieee TRANSACTIONS ON visualization AND COMPUTER graphics 2006年第5期12卷 765-771页

作者： Mehler, Andrew Bao, Yunfan Li, Xin Wang, Yue Skiena, Steven SUNY Stony Brook Dept Comp Sci Stony Brook NY 11794 USA

People in different places talk about different things. This interest distribution is reflected by the newspaper articles circulated in a particular area. We use data from our large-scale newspaper analysis system (Lydia) to make entity datamaps, a spatial visualization of the interest in a given named entity. Our goal is to identify entities which display regional biases. We develop a model of estimating the frequency of reference of an entity in any given city from the reference frequency centered in surrounding cities, and techniques for evaluating the spatial significance of this distribution.

关键词： GIS geographic visualization text and document visualization information analytics WWW data visualization spidering newspapers

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Towards visualization Scalability through Time Intervals and Hierarchical Organization of Monitoring data 09

Towards Visualization Scalability through Time Intervals and...

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9th ieee/ACM International symposium on Cluster Computing and the Grid

作者： Schnorr, Lucas Mello Huard, Guillaume Alexandre Navaux, Philippe Olivier Univ Fed Rio Grande do Sul Inst Informat BR-91501970 Porto Alegre RS Brazil Univ Grenoble 1 LIG Lab CNRS INRIA Moais res Team F-38000 Grenoble France

ISBN: (纸本)9781424439355

Highly distributed systems such as Grids are used today to the execution of large-scale parallel applications. The behavior analysis of these applications is not trivial. The complexity appears because of the event correlation among processes, external influences like time-sharing mechanisms and saturation of network links, and also the amount of data that registers the application behavior Almost all visualization tools to analysis of parallel applications offer a space-time representation of the application behavior This paper presents a novel technique that combines traces front grid applications with a treemap visualization of the data. With this combination, we dynamically create an annotated hierarchical structure that represents the application behavior for the selected time interval. The experiments in the grid show that we can readily use our technique to the analysis of large-scale parallel applications with thousands of processes.

关键词： data visualization Scalability Monitoring large-scale systems Yarn Time sharing computer systems Application software Concurrent computing Distributed computing Grid computing

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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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Making Many-to-Many parallel Coordinate Plots Scalable by Asymmetric Biclustering 10

Making Many-to-Many Parallel Coordinate Plots Scalable by As...

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ieee Pacific visualization symposium (ieee PacificVis)

作者： Wu, Hsiang-Yun Niibe, Yusuke Watanabe, Kazuho Takahashi, Shigeo Uemura, Makoto Fujishiro, Issei Keio Univ Tokyo Japan Toyohashi Univ Technol Toyohashi Aichi Japan Univ Aizu Aizu Wakamatsu Fukushima Japan Hiroshima Univ Hiroshima Japan

ISBN: (纸本)9781509057382

datasets obtained through recently advanced measurement techniques tend to possess a large number of dimensions. This leads to explosively increasing computation costs for analyzing such datasets, thus making formulation and verification of scientific hypotheses very difficult. Therefore, an efficient approach to identifying feature subspaces of target datasets, that is, the subspaces of dimension variables or subsets of the data samples, is required to describe the essence hidden in the original dataset. This paper proposes a visual data mining framework for supporting semiautomatic data analysis that builds upon asymmetric biclustering to explore highly correlated feature subspaces. For this purpose, a variant of parallel coordinate plots, many-to-many parallel coordinate plots, is extended to visually assist appropriate selections of feature subspaces as well as to avoid intrinsic visual clutter. In this framework, biclustering is applied to dimension variables and data samples of the dataset simultaneously and asymmetrically. A set of variable axes are projected to a single composite axis while data samples between two consecutive variable axes are bundled using polygonal strips. This makes the visualization method scalable and enables it to play a key role in the framework. The effectiveness of the proposed framework has been empirically proven, and it is remarkably useful for many-to-many parallel coordinate plots.

关键词： data visualization Correlation visualization data mining Feature extraction Clustering algorithms Clutter

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visualization and pattern identification in large scale time series data

Visualization and pattern identification in large scale time...

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

作者： Holtz, Steve Valle, Guillermo Howard, Jessica Morreale, Patricia Computer Science Kean University United States NJCSTM Kean University United States

ISBN: (纸本)9781467301541

visualization of massively large datasets presents two significant problems. First, the dataset must be prepared for visualization, and traditional dataset manipulation methods fail due to lack of temporary storage or memory. The second problem is the presentation of the data in the visual media, particularly real-time visualization of streaming time series data. An ongoing research project addresses both these problems, using data from two national repositories. This work is presented here, with the results of the current effort summarized and future plans, including 3D visualization, outlined. © 2011 ieee.

关键词： visualization

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Correlating Sub-Phenomena in Performance datainthe Frequency Domain 6

Correlating Sub-Phenomena in Performance Datainthe Frequency...

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6th ieee symposium on large data Analysis and visualization (LDAV)

作者： Vierjahn, Tom Hermanns, Marc-Andre Mohr, Bernd Mueller, Matthias S. Kuhlen, Torsten W. Hentschel, Bernd Rhein Westfal TH Aachen Visual Comp Inst Aachen Germany Rhein Westfal TH Aachen Chair High Performance Comp Aachen Germany Julich Supercomp Ctr Julich Germany JARA High Performance Comp Aachen Germany

ISBN: (纸本)9781509056590

Finding and understanding correlated performance behaviour of the individual functions of massively parallel high-performance computing (HPC) applications is a time-consuming task. In this poster, we propose filtered correlation analysis for automatically locating interdependencies in call-path performance profiles. Transforming the data into the frequency domain splits a performance phenomenon into sub-phenomena to be correlated separately. We provide the mathematical framework and an overview over the visualization, and we demonstrate the effectiveness of our technique.

关键词： D.2.8 [Software Engineering]: Metrics/Measurement-Performance measures I.5.4.m [Pattern Recognition]: Applications-Signal processing I.6.9 [Simulation Modeling and visualization]: visualization

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Hierarchical pixel bar charts

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ieee TRANSACTIONS ON visualization AND COMPUTER graphics 2002年第3期8卷 255-269页

作者： Keim, DA Hao, MC Dayal, U AT&T Corp Res Florham Pk NJ USA Univ Constance D-7750 Constance Germany Hewlett Packard Res Labs Palo Alto CA USA

Simple presentation graphics are intuitive and easy-to-use, but only show highly aggregated data. Bar charts, for example, only show a rather small number of data values and x-y-plots often have a high degree of overlap. Presentation techniques are often chosen depending on the considered data type-bar charts, for example, are used for categorical data and x-y plots are used for numerical data. In this article, we propose a combination of traditional bar charts and x-y-plots, which allows the visualization of large amounts of data with categorical and numerical data. The categorical data dimensions are used for the partitioning into the bars and the numerical data dimensions are used for the ordering arrangement within the bars. The basic idea is to use the pixels within the bars to present the detailed information of the data records. Our so-called pixel bar charts retain the intuitiveness of traditional bar charts while applying the principle of x-y charts within the bars. In many applications, a natural hierarchy is defined on the categorical data dimensions such as time, region, or product type. In hierarchical pixel bar charts, the hierarchy is exploited to split the bars for selected portions of the hierarchy. Our application to a number of real-world e-business and Web services data sets shows the wide applicability and usefulness of our new idea.

关键词： information visualization multidimensional data visualization visual data exploration and data mining very large multiattributes data sets hierarchical visualization

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Real-Time Visual Analysis of High-Volume Social Media Posts

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ieee TRANSACTIONS ON visualization AND COMPUTER graphics 2022年第1期28卷 879-889页

作者： Knittel, Johannes Koch, Steffen Tang, Tan Chen, Wei Wu, Yingcai Liu, Shixia Ertl, Thomas Univ Stuttgart Stuttgart Germany Zhejiang Univ State Key Lab CAD&CG Hangzhou Peoples R China Tsinghua Univ Beijing Peoples R China

Breaking news and first-hand reports often trend on social media platforms before traditional news outlets cover them. The real-time analysis of posts on such platforms can reveal valuable and timely insights for journalists, politicians, business analysts, and first responders, but the high number and diversity of new posts pose a challenge. In this work, we present an interactive system that enables the visual analysis of streaming social media data on a large scale in real-time. We propose an efficient and explainable dynamic clustering algorithm that powers a continuously updated visualization of the current thematic landscape as well as detailed visual summaries of specific topics of interest. Our parallel clustering strategy provides an adaptive stream with a digestible but diverse selection of recent posts related to relevant topics. We also integrate familiar visual metaphors that are highly interlinked for enabling both explorative and more focused monitoring tasks. Analysts can gradually increase the resolution to dive deeper into particular topics. In contrast to previous work, our system also works with non-geolocated posts and avoids extensive preprocessing such as detecting events. We evaluated our dynamic clustering algorithm and discuss several use cases that show the utility of our system.

关键词： Social networking (online) Heuristic algorithms data visualization Clustering algorithms Real-time systems Text analysis Visual analytics Social media analysis dynamic clustering streaming data

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