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. T...
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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.
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 ...
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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.
Significant advances have been made in time-varying dataanalysis and visualization, mainly in improving our ability to identify temporal trends and classify the underlying data. However, the ability to perform cost-e...
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ISBN:
(纸本)9781467347976
Significant advances have been made in time-varying dataanalysis and visualization, mainly in improving our ability to identify temporal trends and classify the underlying data. However, the ability to perform cost-effective data querying and indexing is often not incorporated, which posts a serious limitation as the size of timevarying data continue to grow. In this paper, we present a new approach that unifies data compacting, indexing and classification into a single framework. We achieve this by transforming the timeactivity curve representation of a time-varying data set into a hierarchical symbolic representation. We further build an indexable version of the data hierarchy, from which we create the iTree for visual representation of the time-varying data. A hyperbolic layout algorithm is employed to draw the iTree with a large number of nodes and provide focus+ context visualization for interaction. We achieve effective querying, searching and tracking of time-varying data sets by enabling multiple coordinated views consisting of the iTree, the symbolic view and the spatial view.
We propose and discuss a paradigm that allows for expressing data-parallel rendering with the classically non-parallel ANARI API. We propose this as a new standard for data-parallel rendering, describe two different i...
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ISBN:
(纸本)9798331516932;9798331516925
We propose and discuss a paradigm that allows for expressing data-parallel rendering with the classically non-parallel ANARI API. We propose this as a new standard for data-parallel rendering, describe two different implementations of this paradigm, and use multiple sample integrations into existing applications to show how easy it is to adopt, and what can be gained from doing so.
Triggers are an emerging strategy for optimizing execution time for in situ analysis. However, their performance characteristics are complex, making it difficult to decide if a particular trigger-based approach is via...
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ISBN:
(纸本)9781665432832
Triggers are an emerging strategy for optimizing execution time for in situ analysis. However, their performance characteristics are complex, making it difficult to decide if a particular trigger-based approach is viable. With this study, we propose a cost model for trigger-based in situ analysis that can assess viability, and we also validate the model's efficacy. Then, once the cost model is established, we apply the model to inform the space of viable approaches, considering variation in simulation code, trigger techniques, and analyses, as well as trigger inspection and fire rates. Real-world values are needed both to validate the model and to use the model to inform the space of viable approaches. We obtain these values by surveying science application teams and by performing runs as large as 2,040 GPUs and 32 billion cells.
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...
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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 largedata 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.
We describe the design and deployment of Many Eyes, a public web site where users may upload data, create interactive visualizations, and carry on discussions. The goal of the site is to support collaboration around v...
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We describe the design and deployment of Many Eyes, a public web site where users may upload data, create interactive visualizations, and carry on discussions. The goal of the site is to support collaboration around visualizations at a large scale by fostering a social style of dataanalysis in which visualizations not only serve as a discovery tool for individuals but also as a medium to spur discussion among users. To support this goal, the site includes novel mechanisms for end-user creation of visualizations and asynchronous collaboration around those visualizations. In addition to describing these technologies, we provide a preliminary report on the activity of our users.
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 po...
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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.
To analyze large amounts of numerical data, one of the most useful approaches is to use scientific visualization to transform them into graphical images. Flow visualization as one of the challenging topics has played ...
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ISBN:
(纸本)9781424433940
To analyze large amounts of numerical data, one of the most useful approaches is to use scientific visualization to transform them into graphical images. Flow visualization as one of the challenging topics has played important roles in oceanic dataanalysis. There are many techniques have been presented in the past decade, but most of them can't get high performance to visualize large-scale flow data in real time. To deduce the computational complexity brought by large flow dataset, feature-based expression will be a helpful way. However, how to get the result images quickly without costing much time for feature extraction and analysis is a very important problem to deal. Based on the common characteristic of flow and the unchangeable scale feather of spiral line, we present a new distributing strategy which needn't locate feature points very accurately and didn't rely on the type of feature fields. The visualization procedure not only can straight forward automatically but also can be changed with user's interactive command. The flow data obtained from the South Sea of China was verified and simulated. The result shows that this method using spiral strategy not templates to setting the seeds to emphasize the interesting fields is much faster and flexible, especially in large-scale flow datavisualization
In recent years online display advertising has grown at a rapid pace. Genome from Yahoo! is the big data buying solution for online display advertising. The goal of our platform is to identify the best opportunity to ...
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