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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...

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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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Interactive visualization of large data Sets

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ieee TRANSACTIONS ON KNOWLEDGE AND data ENGINEERING 2016年第8期28卷 2142-2157页

作者： Godfrey, Parke Gryz, Jarek Lasek, Piotr York Univ Dept Elect Engn & Comp Sci Toronto ON Canada York Univ Toronto ON Canada Rzeszow Univ Rzeszow Poland

visualization provides a powerful means for data analysis. But to be practical, visual analytics tools must support smooth and flexible use of visualizations at a fast rate. This becomes increasingly onerous with the ever-increasing size of real-world datasets. First, large databases make interaction more difficult once query response time exceeds several seconds. Second, any attempt to show all data points will overload the visualization, resulting in chaos that will only confuse the user. Over the last few years, substantial effort has been put into addressing both of these issues and many innovative solutions have been proposed. Indeed, data visualization is a topic that is too large to be addressed in a single survey paper. Thus, we restrict our attention here to interactive visualization of large data sets. Our focus then is skewed in a natural way towards query processing problem-provided by an underlying database system-rather than to the actual data visualization problem.

关键词： database visualization data aggregation data indexing interactive visualization big data

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A Customized Validator Recommender System for PoS Networks Using Similarity-Based Circular visualization 14

A Customized Validator Recommender System for PoS Networks U...

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14th symposium on large data analysis and visualization

作者： Lee, Jaeuk Kim, Jisu Han, Hyunwoo Lee, Kyungwon Ajou Univ Dept Digital Media Suwon South Korea Ajou Univ Dept Business Intelligence Suwon South Korea Stamper Co Ltd Seoul South Korea

ISBN: (纸本)9798331516932;9798331516925

This study analyzes the impact of validator behavior on investor rewards in proof-of-stake (PoS) based blockchain networks and proposes a visualization system to assist investors in selecting appropriate validators. This system enables personalized evaluations through five adjustable indicators tailored to the investor's preferences. By utilizing similarity-based circular visualizations and radar charts, it facilitates the selection and comparison of validators. Additionally, it provides time-series data-based line graphs and raw data-based table views to support detailed comparative analysis among validators. The introduction of such a multifaceted evaluation methodology in staking investments is expected to contribute to the formation of user-customized portfolios and the establishment of optimized investment strategies.

关键词： Circular visualization Blockchain Networks Similarity-Based analysis Customized Recommendations

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Structure-based brushes: A mechanism for navigating hierarchically organized data and information spaces

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ieee TRANSACTIONS ON visualization AND COMPUTER GRAPHICS 2000年第2期6卷 150-159页

作者： Fua, YH Ward, MO Rundensteiner, EA Worcester Polytech Inst Dept Comp Sci Worcester MA 01609 USA

Interactive selection is a critical component in exploratory visualization, allowing users to isolate subsets of the displayed information for highlighting, deleting, analysis, or focused investigation. Brushing, a popular method for implementing the selection process, has traditionally been performed in either screen space or data space. In this paper, we introduce an alternate, and potentially powerful, mode of selection that we term structure-based brushing, for selection in data sets with natural or imposed structure. Our initial implementation has focused on hierarchically structured data, specifically very large multivariate data sets structured via hierarchical clustering and partitioning algorithms. The structure-based brush allows users to navigate hierarchies by specifying focal extents and level-of-detail on a visual representation of the structure. Proximity-based coloring, which maps similar colors to data that are closely related within the structure, helps convey both structural relationships and anomalies. We describe the design and implementation of our structure-based brushing tool. We also validate its usefulness using two distinct hierarchical visualization techniques, namely hierarchical parallel coordinates and tree-maps. Finally, we discuss relationships between different classes of brushes and identify methods by which structure-based brushing could be extended to alternate data structures.

关键词： brushing hierarchical representation interactive selection exploratory data analysis

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Reclaiming the Horizon: Novel visualization Designs for Time-Series data with large Value Ranges

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ieee TRANSACTIONS ON visualization AND COMPUTER GRAPHICS 2024年第1期30卷 1161-1171页

作者： Braun, Daniel Borgo, Rita Sondag, Max von Landesberger, Tatiana Univ Cologne Cologne Germany Kings Coll London London England

introduce two novel visualization designs to support practitioners in performing identification and discrimination tasks on large value ranges (i.e., several orders of magnitude) in time-series data: (1) The order of magnitude horizon graph, which extends the classic horizon graph;and (2) the order of magnitude line chart, which adapts the log-line chart. These new visualization designs visualize large value ranges by explicitly splitting the mantissa m and exponent e of a value v = m 10(e). We evaluate our novel designs against the most relevant state-of-the-art visualizations in an empirical user study. It focuses on four main tasks commonly employed in the analysis of time-series and large value ranges visualization: identification, discrimination, estimation, and trend detection. For each task we analyze error, confidence, and response time. The new order of magnitude horizon graph performs better or equal to all other designs in identification, discrimination, and estimation tasks. Only for trend detection tasks, the more traditional horizon graphs reported better performance. Our results are domain-independent, only requiring time-series data with large value ranges.

关键词： data visualization Task analysis Image color analysis Bars Standards Market research Estimation visualization techniques time-series design study orders of magnitude logarithmic scale

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Low-Overhead In Situ visualization Using Halo Replay 9

Low-Overhead In Situ Visualization Using Halo Replay

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

作者： Ames, Jeff Rizzi, Silvio Insley, Joseph Patel, Saumil Hernandez, Benjamin Draeger, Erik W. Randles, Amanda Duke Univ Durham NC 27706 USA Argonne Natl Lab Argonne IL 60439 USA Northern Illinois Univ De Kalb IL 60115 USA Oak Ridge Natl Lab Oak Ridge TN USA Lawrence Livermore Natl Lab Livermore CA USA

ISBN: (纸本)9781728126050

In situ visualization and analysis is of increasing importance as the compute and I/O gap further widens with the advance to exascale capable computing. Yet, in situ methods impose resource constraints leading to the difficult task of balancing simulation code performance and the quality of analysis. Applications with tightly-coupled in situ visualization often achieve performance through spatial and temporal downsampling, a tradeoff which risks not capturing transient phenomena at sufficient fidelity. Determining a priori visualization parameters such as sampling rate is difficult without time and resource intensive experimentation. We present a method for reducing resource contention between in situ visualization and stencil codes on heterogeneous systems. This method permits full resolution replay through recording halos and the communication-free reconstruction of interior values uncoupled from the main simulation. We apply this method in the computational fluid dynamics (CFD) code HARVEY [1] on the Summit supercomputer. We demonstrate minimal-overhead, in situ visualization relative to simulation alone, and compare the Halo Replay performance to tightly-coupled in situ approaches.

关键词： data reduction scientific visualization locality parallelism stencils

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SwiftTuna: Responsive and Incremental Visual Exploration of large-scale Multidimensional data 10

SwiftTuna: Responsive and Incremental Visual Exploration of ...

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ieee Pacific visualization symposium (ieee PacificVis)

作者： Jo, Jaemin Kim, Wonjae Yoo, Seunghoon Kim, Bohyoung Seo, Jinwook Seoul Natl Univ Seoul South Korea Hankuk Univ Foreign Studies Seoul South Korea

ISBN: (纸本)9781509057382

For interactive exploration of large-scale data, a preprocessing scheme (e.g., data cubes) has often been used to summarize the data and provide low-latency responses. However, such a scheme suffers from a prohibitively large amount of memory footprint as more dimensions are involved in querying, and a strong prerequisite that specific data structures have to be built from the data before querying. In this paper, we present SwiftTuna, a holistic system that streamlines the visual information seeking process on large-scale multidimensional data. SwiftTuna exploits an in-memory computing engine, Apache Spark, to achieve both scalability and performance without building precomputed data structures. We also present a novel interactive visualization technique, tailed charts, to facilitate large-scale multidimensional data exploration. To support responsive querying on large-scale data, SwiftTuna leverages an incremental processing approach, providing immediate low-fidelity responses (i.e., prompt responses) as well as delayed high-fidelity responses (i.e., incremental responses). Our performance evaluation demonstrates that SwiftTuna allows data exploration of a real-world dataset with four billion records while preserving the latency between incremental responses within a few seconds.

关键词： Information visualization exploratory analysis large-scale data exploration scalability incremental visualization

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Multiscale visualization using data cubes

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ieee TRANSACTIONS ON visualization AND COMPUTER GRAPHICS 2003年第2期9卷 176-187页

作者： Stolte, C Tang, D Hanrahan, P Stanford Univ Dept Comp Sci Stanford CA 94305 USA

Most analysts start with an overview of the data before gradually refining their view to be more focused and detailed. Multiscale pan-and-zoom systems are effective because they directly support this approach. However, generating abstract overviews of large data sets is difficult and most systems take advantage of only one type of abstraction: visual abstraction. Furthermore, these existing systems limit the analyst to a single zooming path on their data and thus to a single set of abstract views, This paper presents: 1) a formalism for describing multiscale visualizations of data cubes with both data and visual abstraction and 2) a method for independently zooming along one or more dimensions by traversing a zoom graph with nodes at different levels of detail. As an example of how to design multiscale visualizations using our system, we describe four design patterns using our formalism. These design patterns show the effectiveness of multiscale visualization of general relational databases.

关键词： multiscale visualization database visualization graphic formalism

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DIVA: A Declarative and Reactive Language for in situ visualization 10

DIVA: A Declarative and Reactive Language for in situ Visual...

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

作者： Wu, Qi Neuroth, Tyson Igouchkine, Oleg Aditya, Konduri Chen, Jacqueline H. Ma, Kwan-Liu Univ Calif Davis Davis CA 95616 USA Indian Inst Sci Bangalore Karnataka India Sandia Natl Labs Livermore CA 94550 USA

ISBN: (纸本)9781728184685

The use of adaptive workflow management for in situ visualization and analysis has been a growing trend in large-scale scientific simulations. However, coordinating adaptive workflows with traditional procedural programming languages can be difficult because system flow is determined by unpredictable scientific phenomena, which often appear in an unknown order and can evade event handling. This makes the implementation of adaptive workflows tedious and error-prone. Recently, reactive and declarative programming paradigms have been recognized as well-suited solutions to similar problems in other domains. However, there is a dearth of research on adapting these approaches to in situ visualization and analysis. With this paper, we present a language design and runtime system for developing adaptive systems through a declarative and reactive programming paradigm. We illustrate how an adaptive workflow programming system is implemented using our approach and demonstrate it with a use case from a combustion simulation.

关键词： Declarative Grammar Domain Specific Language In Situ visualization

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