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检索条件"任意字段=IEEE Symposium on Large Data Analysis and Visualization"

共 15528 条记录，以下是331-340 订阅

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Parallel Partial Reduction for large-Scale data analysis and visualization 8

Parallel Partial Reduction for Large-Scale Data Analysis and...

引用

8th ieee symposium on large-Scale data analysis and visualization (LDAV)

作者： He, Wenbin Guo, Hanqi Peterka, Tom Di, Sheng Cappello, Franck Shen, Han-Wei Ohio State Univ Dept Comp Sci & Engn Columbus OH 43210 USA Argonne Natl Lab Math & Comp Sci Div Lemont IL USA

ISBN: (纸本)9781538668733

We present a novel partial reduction algorithm to aggregate sparsely distributed intermediate results that are generated by data-parallel analysis and visualization algorithms. Applications of partial reduction include flow trajectory analysis, big data online analytical processing, and volume rendering. Unlike traditional full parallel reduction that exchanges dense data across all processes, the purpose of partial reduction is to exchange only intermediate results that correspond to the same query, such as line segments of the same flow trajectory. To this end, we design a three-stage algorithm that minimizes the communication cost: (1) partitioning the result space into groups;(2) constructing and optimizing the reduction partners for each group;and (3) initiating collective reduction operations for all groups concurrently. Both theoretical and empirical analyses show that our algorithm outperforms the traditional methods when the intermediate results are sparsely distributed. We also demonstrate the effectiveness of our algorithm for flow visualization, big log data analysis, and volume rendering.

关键词： I.3.1 [COMPUTER GRAPHICS]: Hardware Architecture-Parallel processing I.3.2 [COMPUTER GRAPHICS]: Graphics Systems-Distributed/network graphics

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Scalable visualization of Discrete Velocity Decompositions Using Spatially Organized Histograms 5

Scalable Visualization of Discrete Velocity Decompositions U...

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

作者： Neuroth, Tyson Sauer, Franz Wang, Weixing Ethier, Stephane Ma, Kwan-Liu Univ Calif Davis Davis CA 95616 USA Princeton Plasma Phys Lab Princeton NJ 08543 USA

ISBN: (纸本)9781467385176

Visualizing the velocity decomposition of a group of objects has applications to many studied data types, such as Lagrangian-based flow data or geospatial movement data. Traditional visualization techniques are often subject to a trade-off between visual clutter and loss of detail, especially in a large scale setting. The use of 2D velocity histograms can alleviate these issues. While they have been used throughout domain specific areas on a basic level, there has been very little work in the visualization community on leveraging them to perform more advanced visualization tasks. In this work, we develop an interactive system which utilizes velocity histograms to visualize the velocity decomposition of a group of objects. In addition, we extend our tool to utilize two schemes for histogram generation: an on-the-fly sampling scheme as well as an in situ scheme to maintain interactivity in extreme scale applications.

关键词： 2D histogram velocity decomposition particle data movement data large-scale data

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data Parallel Hypersweeps for in Situ Topological analysis 10

Data Parallel Hypersweeps for in Situ Topological Analysis

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10th ieee symposium on large data analysis and visualization (LDAV)

作者： Hristov, Petar Weber, Gunther H. Carr, Hamish A. Rubel, Oliver Ahrens, James P. Univ Leeds Leeds W Yorkshire England Lawrence Berkeley Natl Lab Berkeley CA USA Univ Calif Davis Davis CA 95616 USA Los Alamos Natl Lab Los Alamos NM USA

ISBN: (纸本)9781728184685

The contour tree is a tool for understanding the topological structure of a scalar field. Recent work has built efficient contour tree algorithms for shared memory parallel computation, driven by the need to analyze large data sets in situ while the simulation is running. Unfortunately, methods for using the contour tree for practical data analysis are still primarily serial, including single isocontour extraction, branch decomposition and simplification. We report data parallel methods for these tasks using a data structure called the hyperstructure and a general purpose approach called a hypersweep. We implement and integrate these methods with a Cinema database that stores features as depth images and with a web server that reconstructs the features for direct visualization.

关键词： branch decomposition contour tree geometric measures in situ scalar field

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Interactive visualization of Terascale data in the Browser: Fact or Fiction? 10

Interactive Visualization of Terascale Data in the Browser: ...

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10th ieee symposium on large data analysis and visualization (LDAV)

作者： Usher, Will Pascucci, Valerio Univ Utah SCI Inst Salt Lake City UT 84112 USA

ISBN: (纸本)9781728184685

Information visualization applications have become ubiquitous, in no small part thanks to the ease of wide distribution and deployment to users enabled by the web browser. Scientific visualization applications, relying on native code libraries and parallel processing, have been less suited to such widespread distribution, as browsers do not provide the required libraries or compute capabilities. In this paper, we revisit this gap in visualization technologies and explore how new web technologies, WebAssembly and WebGPU, can be used to deploy powerful visualization solutions for large-scale scientific data in the browser. In particular, we evaluate the programming effort required to bring scientific visualization applications to the browser through these technologies and assess their competitiveness against classic native solutions. As a main example, we present a new GPU-driven isosurface extraction method for block-compressed data sets, that is suitable for interactive isosurface computation on large volumes in resource-constrained environments, such as the browser. We conclude that web browsers are on the verge of becoming a competitive platform for even the most demanding scientific visualization tasks, such as interactive visualization of isosurfaces from a 1TB DNS simulation. We call on researchers and developers to consider investing in a community software stack to ease use of these upcoming browser features to bring accessible scientific visualization to the browser.

关键词： Web Applications Parallel Isosurface Extraction

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Discovery of Rating Fraud with Real-Time Streaming Visual Analytics 12

Discovery of Rating Fraud with Real-Time Streaming Visual An...

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

作者： Webga, Kodzo Lu, Aidong Univ N Carolina Charlotte NC 28223 USA

ISBN: (纸本)9781467375993

The rating fraud in online e-commerce stores targets at receiving large revenues through boosting the popularity of selected items with fake ratings. The challenges of detecting rating frauds come from discovering small scale abnormal activities in a large amount of data and detecting frauds in a time-critical manner from online rating streams. This paper presents a real-time visual analytics system that consists of two essential components: a server for automatically handling data streams and a visual analytics interface for performing interactive analysis. Based on the features of rating frauds, we present a detection solution which balances computationally expensive algorithms and interactive analysis between the server and analysts. Specifically, our detection system filters data through performing an initial suspicion level detection on the server, and analysts can combine different statistical analysis of the user / item matrix through a co-mapped singular value decomposition (SVD) diagram, re-ordered matrix representation, and the temporal view. We demonstrate our approach with case studies of different fraud scenarios and show that rating frauds can be effectively detected.

关键词： Rating fraud fraud detection security visualization streaming visualization

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Block-Parallel data analysis with DIY2 6

Block-Parallel Data Analysis with DIY2

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

作者： Morozov, Dmitriy Peterka, Tom Lawrence Berkeley Natl Lab Berkeley CA USA Argonne Natl Lab Argonne IL 60439 USA

ISBN: (纸本)9781509056590

DIY2 is a programming model and runtime for block-parallel analytics on distributed-memory machines. Its main abstraction is block-structured data parallelism: data are decomposed into blocks;blocks are assigned to processing elements (processes or threads);computation is described as iterations over these blocks, and communication between blocks is defined by reusable patterns. By expressing computation in this general form, the DIY2 runtime is free to optimize the movement of blocks between slow and fast memories (disk and flash vs. DRAM) and to concurrently execute blocks residing in memory with multiple threads. This enables the same program to execute in-core, out-of-core, serial, parallel, single-threaded, multithreaded, or combinations thereof. This paper describes the implementation of the main features of the DIY2 programming model and optimizations to improve performance. DIY2 is evaluated on complete analysis codes.

关键词： Computational modeling Runtime Programming Analytical models Message systems data models data analysis Runtime Computer based information systems data models Programming analysis MODELS Computational modeling data analysis Blocks memory machine

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Maximal Clique Enumeration with data-Parallel Primitives 7

Maximal Clique Enumeration with Data-Parallel Primitives

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ieee 7th symposium on large data analysis and visualization (LDAV)

作者： Lessley, Brenton Perciano, Talita Mathai, Manish Childs, Hank Bethel, E. Wes Univ Oregon Eugene OR 97403 USA Lawrence Berkeley Natl Lab Berkeley CA USA

ISBN: (纸本)9781538606179

The enumeration of all maximal cliques in an undirected graph is a fundamental problem arising in several research areas. We consider maximal clique enumeration on shared-memory, multi-core architectures and introduce an approach consisting entirely of data-parallel operations, in an effort to achieve efficient and portable performance across different architectures. We study the performance of the algorithm via experiments varying over benchmark graphs and architectures. Overall, we observe that our algorithm achieves up to a 33-time speedup and 9-time speedup over state-of-the-art distributed and serial algorithms, respectively, for graphs with higher ratios of maximal cliques to total cliques. Further, we attain additional speedups on a GPU architecture, demonstrating the portable performance of our data-parallel design.

关键词： Arrays Algorithm design and analysis Vegetation data visualization Graphics processing units

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A Lifeline-Based Approach for Work Requesting and Parallel Particle Advection 9

A Lifeline-Based Approach for Work Requesting and Parallel P...

引用

9th ieee symposium on large data analysis and visualization (LDAV)

作者： Binyahib, Roba Pugmire, David Norris, Boyana Childs, Hank Univ Oregon Eugene OR 97403 USA Oak Ridge Natl Lab Oak Ridge TN USA

ISBN: (纸本)9781728126050

Particle advection. a fundamental building block for many flow visualization algorithms, is very difficult to parallelize efficiently. That said, work requesting is a promising technique to improve parallel performance for particle advection. With this work, we introduce a new work requesting-based method which uses the lifeline scheduling method. To evaluate the impact of this new algorithm, we ran 92 experiments, running at concurrencies as high as 8192 cores, data sets as large as 17 billion cells, and as many as 16 million particles, comparing against other work requesting scheduling methods. Overall, our results show that Lifeline has significantly less idle time than other approaches, since it reduces the number of failed attempts to request work.

关键词： Work requesting visualization Parallel particle advection Lifeline-Based scheduling

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CEDARS: Combined Exploratory data analysis Recommender System 5

CEDARS: Combined Exploratory Data Analysis Recommender Syste...

引用

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

作者： Livingston, Mark A. Russell, Stephen Decker, Jonathan W. Leadbetter, Eric Gilliam, Antonio

ISBN: (纸本)9781467385176

We present a framework for recommender systems (RS) to support exploratory data analysis (EDA) in analytical decision making. EDA helps the domain expert, often not a statistical expert, discover interesting relationships between variables and thus be motivated to explain the data. By capturing the behavior of expert analysts in EDA, RS could advise domain experts of "standard" analytical operations and suggest operations novel to the domain but consistent in analytical goals with requested operations. We enhance our framework with rules that encapsulate standard analytical practice and by incorporating user preferences. We present a scalable framework architecture, which we implemented in a prototype system, and discuss two use cases where the prototype was exercised, analyzing data from image analysis and analyzing eye tracking data.

关键词： H.5.2 [Information Interfaces and Presentation]: User Interfaces ??? Graphical user interfaces I.2.6 [Artificial Intelligence]: Learning ??? Concept learning Exploratory data analysis Recommender systems Frameworks Electronic design automation and methodology European Defence Agency EDA gene electronic design automation User preferences Professional personnel ANALYTICAL data PROTOTYPE Surgery image analysis

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Contour Forests: Fast Multi-threaded Augmented Contour Trees 6

Contour Forests: Fast Multi-threaded Augmented Contour Trees

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

作者： Gueunet, Charles Fortin, Pierre Jomier, Julien Tierny, Julien UPMC Univ Paris 06 Sorbonne Univ CNRS Kitware SASLIP6 UMR 7606 Paris France UPMC Univ Paris 06 Sorbonne Univ CNRS LIP6 UMR 7606 Paris France Kitware SAS Villeurbanne France

ISBN: (纸本)9781509056590

This paper presents a new algorithm for the fast, shared memory multi-threaded computation of contour trees on tetrahedral meshes. In contrast to previous multi-threaded algorithms, our technique computes the augmented contour tree. Such an augmentation is required to enable the full extent of contour tree based applications, including for instance data segmentation. Our approach relies on a range-driven domain partitioning. We show how to exploit such a partitioning to rapidly compute contour forests. We also show how such forests can be efficiently turned into the output contour tree. We report performance numbers that compare our approach to a reference sequential implementation for the computation of augmented contour trees. These experiments demonstrate the run-time efficiency of our approach. We demonstrate the utility of our approach with several data segmentation tasks. We also provide a lightweight VTK-based C++ implementation of our approach for reproduction purposes.

关键词： Vegetation data analysis Algorithm design and analysis Isosurfaces Feature extraction data mining contour Algorithm design and analysis Isosurfaces Feature extraction data mining vegetation data analysis data partitioning Trees

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