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.
This paper describes the adaptation to a distributed computational setting of a well-scaling parallel algorithm for computing Morse-Smale segmentations based on path compression. Additionally, we extend the algorithm ...
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ISBN:
(纸本)9798331516932;9798331516925
This paper describes the adaptation to a distributed computational setting of a well-scaling parallel algorithm for computing Morse-Smale segmentations based on path compression. Additionally, we extend the algorithm to efficiently compute connected components in distributed structured and unstructured grids, based either on the connectivity of the underlying mesh or a feature mask. Our implementation is seamlessly integrated with the distributed extension of the Topology ToolKit (TTK), ensuring robust performance and scalability. To demonstrate the practicality and efficiency of our algorithms, we conducted a series of scaling experiments on large-scaledatasets, with sizes of up to 40963 vertices on up to 64 nodes and 768 cores.
Functional approximation as a high-order continuous representation provides a more accurate value and gradient query compared to the traditional discrete volume representation. Volume visualization directly rendered f...
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ISBN:
(纸本)9798331516932;9798331516925
Functional approximation as a high-order continuous representation provides a more accurate value and gradient query compared to the traditional discrete volume representation. Volume visualization directly rendered from functional approximation generates high-quality rendering results without high-order artifacts caused by trilinear interpolations. However, querying an encoded functional approximation is computationally expensive, especially when the input dataset is large, making functional approximation impractical for interactive visualization. In this paper, we proposed a novel functional approximation multi-resolution representation, Adaptive-FAM, which is lightweight and fast to query. We also design a GPU-accelerated out-of-core multi-resolution volume visualization framework that directly utilizes the Adaptive-FAM representation to generate high-quality rendering with interactive responsiveness. Our method can not only dramatically decrease the caching time, one of the main contributors to input latency, but also effectively improve the cache hit rate through prefetching. Our approach significantly outperforms the traditional function approximation method in terms of input latency while maintaining comparable rendering quality.
Scientists often explore and analyze large-scale scientific simulation data by leveraging 2-D and 3-D visualizations. The data and tasks can be complex and therefore best supported using myriad display technologies, f...
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Scientists often explore and analyze large-scale scientific simulation data by leveraging 2-D and 3-D visualizations. The data and tasks can be complex and therefore best supported using myriad display technologies, from mobile devices to large high-resolution display walls to virtual reality headsets. Using a simulation of neuron connections in the human brain provided for the 2023 ieee Scientific visualization Contest, we present our work leveraging various web technologies to create a multiplatform scientific visualization application. Users can spread visualization and interaction across multiple devices to support flexible user interfaces and both colocated and remote collaboration. Drawing inspiration from responsive web design principles, this work demonstrates that a single codebase can be adapted to develop scientific visualization applications that operate everywhere.
Translating natural language to visualization (NL2VIS) has shown great promise for visual dataanalysis, but it remains a challenging task that requires multiple low-level implementations, such as natural language pro...
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Translating natural language to visualization (NL2VIS) has shown great promise for visual dataanalysis, but it remains a challenging task that requires multiple low-level implementations, such as natural language processing and visualization design. Recent advancements in pre-trained large language models (LLMs) are opening new avenues for generating visualizations from natural language. However, the lack of a comprehensive and reliable benchmark hinders our understanding of LLMs' capabilities in visualization generation. In this paper, we address this gap by proposing a new NL2VIS benchmark called VisEval. Firstly, we introduce a high-quality and large-scaledataset. This dataset includes 2,524 representative queries covering 146 databases, paired with accurately labeled ground truths. Secondly, we advocate for a comprehensive automated evaluation methodology covering multiple dimensions, including validity, legality, and readability. By systematically scanning for potential issues with a number of heterogeneous checkers, VisEval provides reliable and trustworthy evaluation outcomes. We run VisEval on a series of state-of-the-art LLMs. Our evaluation reveals prevalent challenges and delivers essential insights for future advancements.
Hydropower turbines are large-scale equipment essential to sustainable energy supply chains, and engineers have few opportunities to examine their internal structure. Our Immersive Analytics (IA) application is part o...
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Hydropower turbines are large-scale equipment essential to sustainable energy supply chains, and engineers have few opportunities to examine their internal structure. Our Immersive Analytics (IA) application is part of a research project that combines and compares simulated water turbine flows and sensor-measured data, looking for data-driven predictions of the lifetime of the mechanical parts of hydroelectric power plants. Our prototype combines spatial and abstract data in an immersive environment in which the user can navigate through a full-scale model of a water turbine, view simulated water flows of three different energy supply conditions, and visualize and interact with sensor-collected data situated at the reference position of the sensors in the actual turbine. In this paper, we detail our design process, which resulted from consultations with domain experts and a literature review, give an overview of our prototype, and present its evaluation, resulting from semi-structured interviews with experts and qualitative thematic analysis. Our findings confirm the current literature that IA applications add value to the presentation and analysis of situated data, as they show that we advance in the design directions for IA applications for domain experts that combine abstract and spatial data, with conclusions on how to avoid skepticism from such professionals.
The conventional method of data exploration primarily relies on 2D and 3D visualization tools. However, with the advent of lower-cost virtual reality (VR) hardware, a transformation is underway. This study presents a ...
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ISBN:
(纸本)9798331516932;9798331516925
The conventional method of data exploration primarily relies on 2D and 3D visualization tools. However, with the advent of lower-cost virtual reality (VR) hardware, a transformation is underway. This study presents a novel data pipeline developed from ParaView (Open source Scientific visualization software) to the Unity Game Engine (Cross platform used to develop interactive contents such as games, animations and in this case VR) to investigate this transition. Specifically, we apply VR technology to the exploration of particle-based scientific datasets, focusing on data generated by a Hardware/Hybrid Accelerated Cosmology Code (HACC) simulation. This project applies VR to the identification of patterns and clusters within HACC particle-based datasets. We enable effective user interaction by integrating VR into the broader field of largedata exploration, which includes features like data interaction, manipulation, and in-depth analysis. We implement custom interactions to enable interrogation of underlying data streams to provide deeper insight.
The reconstruction and novel view synthesis of dynamic scenes recently gained increased attention. As reconstruction from large-scale multi-view data involves immense memory and computational requirements, recent benc...
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The reconstruction and novel view synthesis of dynamic scenes recently gained increased attention. As reconstruction from large-scale multi-view data involves immense memory and computational requirements, recent benchmark datasets provide collections of single monocular views per timestamp sampled from multiple (virtual) cameras. We refer to this form of inputs as monocularized data. Existing work shows impressive results for synthetic setups and forward-facing real-world data, but is often limited in the training speed and angular range for generating novel views. This paper addresses these limitations and proposes a new method for full 360 degrees inward-facing novel view synthesis of non-rigidly deforming scenes. At the core of our method are: 1) An efficient deformation module that decouples the processing of spatial and temporal information for accelerated training and inference;and 2) A static module representing the canonical scene as a fast hash-encoded neural radiance field. In addition to existing synthetic monocularized data, we systematically analyze the performance on real-world inward-facing scenes using a newly recorded challenging dataset sampled from a synchronized large-scale multi-view rig. In both cases, our method is significantly faster than previous methods, converging in less than 7 minutes and achieving real-time framerates at 1 K resolution, while obtaining a higher visual accuracy for generated novel views.
In geo-related fields such as urban informatics, atmospheric science, and geography, large-scale spatial time (ST) series (i.e., geo-referred time series) are collected for monitoring and understanding important spati...
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In geo-related fields such as urban informatics, atmospheric science, and geography, large-scale spatial time (ST) series (i.e., geo-referred time series) are collected for monitoring and understanding important spatiotemporal phenomena. ST series visualization is an effective means of understanding the data and reviewing spatiotemporal phenomena, which is a prerequisite for in-depth dataanalysis. However, visualizing these series is challenging due to their largescales, inherent dynamics, and spatiotemporal nature. In this study, we introduce the notion of patterns of evolution in ST series. Each evolution pattern is characterized by 1) a set of ST series that are close in space and 2) a time period when the trends of these ST series are correlated. We then leverage Storyline techniques by considering an analogy between evolution patterns and sessions, and finally design a novel visualization called GeoChron, which is capable of visualizing large-scale ST series in an evolution pattern-aware and narrative-preserving manner. GeoChron includes a mining framework to extract evolution patterns and two-level visualizations to enhance its visual scalability. We evaluate GeoChron with two case studies, an informal user study, an ablation study, parameter analysis, and running time analysis.
The large-scale motions in 3D turbulent channel flows, known as Turbulent Superstructures (TSS), play an essential role in the dynamics of small-scale structures within the turbulent boundary layer. However, as of tod...
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The large-scale motions in 3D turbulent channel flows, known as Turbulent Superstructures (TSS), play an essential role in the dynamics of small-scale structures within the turbulent boundary layer. However, as of today, there is no common agreement on the spatial and temporal relationships between these multiscale structures. We propose a novel space-time visualization technique for analyzing the temporal evolution of these multiscale structures in their spatial context and, thus, to further shed light on the conceptually different explanations of their dynamics. Since the temporal dynamics of TSS are believed to influence the structures in the turbulent boundary layer, we propose a combination of a 2D space-time velocity plot with an orthogonal 2D plot of projected 3D flow structures, which can interactively span the time and the space axis. Besides flow structures indicating the fluid motion, we propose showing the variations in derived fields as an additional source of explanation. The relationships between the structures in different spatial and temporal scales can be more effectively resolved by using various filtering operations and image registration algorithms. To reduce the information loss due to the non-injective nature of projection, spatial information is encoded into transparency or color. Since the proposed visualization is heavily demanding computational resources and memory bandwidth to stream unsteady flow fields and instantly compute derived 3D flow structures, the implementation exploits data compression, parallel computation capabilities, and high memory bandwidth on recent GPUs via the CUDA compute library.
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