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ieee COMPUTER GRAPHICS AND APPLICATIONS 2005年第1期25卷 12-14页

作者： Eick, SG Univ Illinois Chicago IL 60680 USA

The article focuses on the 10th anniversary of the ieee Computer Society's Information visualization symposium. At 10, information visualization is vigorous and thriving. In the early years, the symposium had difficulty attracting enough good papers to fill the slots. Today it receives so many excellent papers that the acceptance rate is low. The field has become an academic success. Some projects have had notable commercial success. Display technologies are in the middle of a massive transition. Conventional CRTs are being swapped for large, megapixel flat-screen displays. This transition started with increasing display sizes in laptops and has accelerated as new as display technologies have moved into widely deployed consumer goods such as high-definition televisions. The result of these technology changes is that screen real estate is increasing. Seventeen- and 21-inch flat- Information visualization is domain specific. The most interesting visualizations solve problems. An emerging class of problem involves analyzing data that arrives in a stream. Massive sensor systems, video cameras, and transaction-processing systems generate real-time data streams.

关键词： CONFERENCES & conventions INFORMATION science COMMUNICATION INFORMATION display systems INFORMATION visualization

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A Visual Analytics Paradigm Enabling Trillion-Edge Graph Exploration 5

A Visual Analytics Paradigm Enabling Trillion-Edge Graph Exp...

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

作者： Wong, Pak Chung Haglin, David Gillen, David Chavarria, Daniel Castellana, Vito Joslyn, Cliff Chappell, Alan Zhang, Song Pacific Northwest Natl Lab Richland WA 99352 USA Mississippi State Univ Mississippi State MS 39762 USA

ISBN: (纸本)9781467385176

We present a visual analytics paradigm and a system prototype for exploring web-scale graphs. A web-scale graph is described as a graph with similar to one trillion edges and similar to 50 billion vertices. While there is an aggressive R&D effort in processing and exploring web-scale graphs among internet vendors such as Facebook and Google, visualizing a graph of that scale still remains an underexplored R&D area. The paper describes a nontraditional peek-and-filter strategy that facilitates the exploration of a graph database of unprecedented size for visualization and analytics. We demonstrate that our system prototype can 1) preprocess a graph with similar to 25 billion edges in less than two hours and 2) support database query and interactive visualization on the processed graph database afterward. Based on our computational performance results, we argue that we most likely will achieve the one trillion edge mark (a computational performance improvement of 40 times) for graph visual analytics in the near future.

关键词： Visual analytics graph visualization web-scale graph NetFlow triple-store RDF SPARQL high-performance computing Linux cluster

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Image and Distribution Based Volume Rendering for large data Sets 11

Image and Distribution Based Volume Rendering for Large Data...

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ieee Pacific visualization symposium (ieee PacificVis)

作者： Wang, Ko-Chih Shareef, Naeem Shen, Han-Wei Ohio State Univ Columbus OH 43210 USA

ISBN: (纸本)9781538614242

Analyzing scientific datasets created from simulations on modern supercomputers is a daunting challenge due to the fast pace at which these datasets continue to grow. Low cost post analysis machines used by scientists to view and analyze these massive datasets are severely limited by their deficiencies in storage bandwidth, capacity, and computational power. Trying to simply move these datasets to these platforms is infeasible. Any approach to view and analyze these datasets on post analysis machines will have to effectively address the inevitable problem of data loss. Image based approaches are well suited for handling very large datasets on low cost platforms. Three challenges with these approaches are how to effectively represent the original data with minimal data loss, analyze the data in regards to transfer function exploration, which is a key analysis tool, and quantify the error from data loss during analysis. We present a novel image based approach using distributions to preserve data integrity. At each view sample, view dependent data is summarized at each pixel with distributions to define a compact proxy for the original dataset. We present this representation along with how to manipulate and render large scale datasets on post analysis machines. We show that our approach is a good trade off between rendering quality and interactive speed and provides uncertainty quantification for the information that is lost.

关键词： big data Distribution based volume rendering

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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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Interactive SNMP Traffic analysis Through Information visualization

Interactive SNMP Traffic Analysis Through Information Visual...

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2010 ieee-IFIP Network Operations and Management symposium

作者： Barbosa, Paulo Teles Granville, Lisandro Zambenedetti Univ Fed Rio Grande do Sul Inst Informat Porto Alegre RS Brazil

ISBN: (纸本)9781424453672

The network management area deals with large amounts of data. Some of its protocols and techniques are not completely understood when it comes to usage patterns and most used features. The understanding of such characteristics is a challenging process, due to the massive data amount involved. This process can be supported by information visualization techniques. These consist in visual representations of data that make use of the unique properties of the human visual system to make insights about it in a more intuitive and effective way. In this context, interactivity has proved itself to be one of the main factors involved in providing such intuitiveness and effectiveness, specially in analysis of large datasets. Nevertheless, few interaction possibilities are available in current network management traffic visualization systems. In this paper we present a set of interactive information visualization techniques adapted to visualize SNMP trace files. We used an insight-based evaluation to show how the presented techniques can aid on the insight achievement process.

关键词： Network management

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Analytical visualization of large-scale data of 3D Cultural Objects

Analytical Visualization of Large-scale data of 3D Cultural ...

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ieee Pacific visualization symposium (PacificVis)

作者： Tanaka, Satoshi Ritsumeikan Univ Kyoto Japan

ISBN: (纸本)9781479928736

For appreciating 3D cultural objects, e. g., artistic statues, traditional vehicles, and historical buildings or cities, visualization has mainly been used in the following styles: (1) realistic computer graphics, (2) artistic computer graphics, and (3) interactive virtual reality. However, for academic studying of 3D cultural objects, these styles are insufficient. Namely, we need more analytical styles that can extract invisible or veiled features of visualized targets. What makes such analytical visualization more important is recent rapid development of range-sensing technology to digitally archive real 3D objects. For example, modern laser scanning produces over 107 or 10(8) 3D point data for one measurement. To extract useful information from such large-scale and complex data, we need (A) precise 3D transparent visualization, (B) superposition of visual assistants, and (C) time-series visualization.

关键词： Computer Graphics [I.3.m]: Miscellaneous Image Processing and Computer Vision [I.4.8]: Scene analysis Range data

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Using visualization to Debug visualization Software

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ieee COMPUTER GRAPHICS AND APPLICATIONS 2010年第6期30卷 67-73页

作者： Laramee, Robert S. Swansea Univ Dept Comp Sci Swansea W Glam Wales

Developing visualization applications is nontrivial and poses special challenges. This is because typical visualization software processes a large amount of data, resulting in large and sometimes very complex data structures. Traditional debugging tools have limited use because they decouple the information they report from the spatiotemporal domain in which unexpected problems occur. This article presents a set of guidelines specifically for debugging visualization software. The guidelines come from experience in developing applications in both industry and research. The key is to exploit the strengths of computer graphics and visualization, while still using good software development practices. [ABSTRACT FROM PUBLISHER]

关键词： Computer bugs computer graphics computer graphics applications computing methodologies data visualization Debugging graphics and multimedia Guidelines Image color analysis methodology and techniques Software visualization

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Evaluating Trace Aggregation for Performance visualization of large Distributed Systems

Evaluating Trace Aggregation for Performance Visualization o...

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ieee International symposium on Performance analysis of Systems and Software (ISPASS)

作者： Lamarche-Perrin, Robin Schnorr, Lucas Mello Vincent, Jean-Marc Demazeau, Yves Univ Grenoble Alpes Lab Informat Grenoble Grenoble France CNRS Lab Informat Grenoble F-75700 Paris France

ISBN: (纸本)9781479936069

Performance analysis through visualization techniques usually suffers semantic limitations due to the size of parallel applications. Most performance visualization tools rely on data aggregation to work at scale, without any attempt to evaluate the loss of information caused by such aggregations. This paper proposes a technique to evaluate the quality of aggregated representations - using measures from information theory - and to optimize such measures in order to build consistent multiresolution representations of large execution traces.

关键词： Distributed Systems visualization information loss unified retrieval models Information Theory representations

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In Situ Prediction Driven Feature analysis in Jet Engine Simulations 11

In Situ Prediction Driven Feature Analysis in Jet Engine Sim...

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ieee Pacific visualization symposium (ieee PacificVis)

作者： Dutta, Soumya Shen, Han-Wei Chen, Jen-Ping Ohio State Univ Columbus OH 43210 USA

ISBN: (纸本)9781538614242

Efficient feature exploration in large-scale data sets using traditional post-hoc analysis approaches is becoming prohibitive due to the bottleneck stemming from I/O and output data sizes. This problem becomes more challenging when an ensemble of simulations are required to run for studying the influence of input parameters on the model output. As a result, scientists are inclining more towards analyzing the data in situ while it resides in the memory. In situ analysis aims at minimizing expensive data movement while maximizing the resource utilization for extraction of important information from the data. In this work, we study the evolution of rotating stall in jet engines using data generated from a large-scale flow simulation under various input conditions. Since the features of interest lack a precise descriptor, we adopt a fuzzy rule-based machine learning algorithm for efficient and robust extraction of such features. For scalable exploration, we advocate for an off-line learning and in situ prediction driven strategy that facilitates in-depth study of the stall. Task-specific information estimated in situ is visualized interactively during the post-hoc analysis revealing important details about the inception and evolution of stall. We verify and validate our method through comprehensive expert evaluation demonstrating the efficacy of our approach.

关键词： In situ analysis machine learning in visualization fuzzy rule-based system multivariate analysis rotating stall big data analytics

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Cartolabe: A Web-Based Scalable visualization of large Document Collections

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ieee COMPUTER GRAPHICS AND APPLICATIONS 2021年第2期41卷 76-87页

作者： Caillou, Philippe Renault, Jonas Fekete, Jean-Daniel Letournel, Anne-Catherine Sebag, Michele Univ Paris Saclay CNRS INRIA LRI F-91190 Gif Sur Yvette France

We describe Cartolabe, a web-based multiscale system for visualizing and exploring large textual corpora based on topics, introducing a novel mechanism for the progressive visualization of filtering queries. Initially designed to represent and navigate through scientific publications in different disciplines, Cartolabe has evolved to become a generic framework and accommodate various corpora, ranging from Wikipedia (4.5M entries) to the French National Debate (4.3M entries). Cartolabe is made of two modules: The first relies on natural language processing methods, converting a corpus and its entities (documents, authors, and concepts) into high-dimensional vectors, computing their projection on the two-dimensional plane, and extracting meaningful labels for regions of the plane. The second module is a web-based visualization, displaying tiles computed from the multidimensional projection of the corpus using the Umap projection method. This visualization module aims at enabling users with no expertise in visualization and data analysis to get an overview of their corpus, and to interact with it: exploring, querying, filtering, panning, and zooming on regions of semantic interest. Three use cases are discussed to illustrate Cartolabe's versatility and ability to bring large-scale textual corpus visualization and exploration to a wide audience.

关键词： data visualization Two dimensional displays Organizations Task analysis Pipelines visualization Navigation

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