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Integrating GPU-Accelerated for Fast large-scale Vessel Trajectories visualization in Maritime IoT Systems

ieee TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2025年第3期26卷 4048-4065页

作者： Liang, Maohan Liu, Kezhong Gao, Ruobin Li, Yan Natl Univ Singapore Dept Civil & Environm Engn Singapore 119077 Singapore Wuhan Univ Technol Sch Nav Wuhan 430062 Peoples R China Northwestern Polytech Univ Sch Marine Sci & Technol Xian 710072 Peoples R China Wuhan Univ State Key Lab Informat Engn Surveying Mapping & Re Wuhan 430072 Peoples R China

With the advancement of satellite communication technology, the maritime Internet of Things (IoT) has made significant progress. As a result, vast amounts of Automatic Identification System (AIS) data from global vessels are transmitted to various maritime stakeholders through Maritime IoT systems. AIS data contains a large amount of dynamic and static information that requires effective and intuitive visualization for comprehensive analysis. However, two major deficiencies challenge current visualization models: a lack of consideration for interactions between distant pixels and low efficiency. To address these issues, we developed a large-scale vessel trajectories visualization algorithm, called the Non-local Kernel Density Estimation (NLKDE) algorithm, which incorporates a non-local convolution process. It accurately calculates the density distribution of vessel trajectories by considering correlations between distant pixels. Additionally, we implemented the NLKDE algorithm under a Graphics Processing Unit (GPU) framework to enable parallel computing and improve operational efficiency. Comprehensive experiments using multiple vessel trajectory datasets show that the NLKDE algorithm excels in vessel trajectory density visualization tasks, and the GPU-accelerated framework significantly shortens the execution time to achieve real-time results. From both theoretical and practical perspectives, GPU-accelerated NLKDE provides technical support for real-time monitoring of vessel dynamics in complex water areas and contributes to constructing maritime intelligent transportation systems. The code for this paper can be accessed at: https://***/maohliang/GPU-NLKDE.

关键词： Trajectory data visualization Graphics processing units Artificial intelligence Convolution Real-time systems Heuristic algorithms Feature extraction data transfer data mining Trajectory visualization non-local convolution graphics processing unit parallel computing maritime Internet of Things

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Low-Cost Post Hoc Reconstruction of HPC Simulations at Full Resolution 13

Low-Cost Post Hoc Reconstruction of HPC Simulations at Full ...

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13th ieee symposium on large data analysis and visualization (LDAV)

作者： Yousef, Ayman Z. Draeger, Erik W. Randles, Amanda Duke Univ Durham NC 27708 USA Lawrence Livermore Natl Lab Livermore CA 94550 USA

ISBN: (纸本)9798350330182

High performance computing has played a pivotal and ongoing role in the field of computational fluid dynamics, enabling the simulation of increasingly larger-scale models. However, this rapid growth in model and simulation size has outpaced the capabilities of input/output (I/O) operations. Consequently, the conventional approach of saving and outputting data to persistent storage for analysis has become increasingly challenging, limiting the benefits of these advanced models. To address this challenge, we present a method for effectively handling massive-scale simulation data, ensuring its persistence at full spatial and temporal resolution for flexible post hoc analysis. We employ an in situ approach that captures interprocess communicated data, compressing cached data to a fraction of the overall simulation domain. We successfully reconstruct subdomains at full spatial and temporal resolution during post-processing through communication-free rerun using the cached halos. We detail the storage requirements of the new approach and demonstrate the substantial reductions in computational resources required to precisely recapitulate data within a local region of interest.

关键词： data analysis in situ

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Out-of-Core Dimensionality Reduction for large data via Out-of-Sample Extensions 14

Out-of-Core Dimensionality Reduction for Large Data via Out-...

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14th symposium on large data analysis and visualization

作者： Reichmann, Luca Haegele, David Weiskopf, Daniel Univ Stuttgart Stuttgart Germany

ISBN: (纸本)9798331516932;9798331516925

Dimensionality reduction (DR) is a well-established approach for the visualization of high-dimensional data sets. While DR methods are often applied to typical DR benchmark data sets in the literature, they might suffer from high runtime complexity and memory requirements, making them unsuitable for large data visualization especially in environments outside of high-performance computing. To perform DR on large data sets, we propose the use of out-of-sample extensions. Such extensions allow inserting new data into existing projections, which we leverage to iteratively project data into a reference projection that consists only of a small manageable subset. This process makes it possible to perform DR out-of-core on large data, which would otherwise not be possible due to memory and runtime limitations. For metric multidimensional scaling (MDS), we contribute an implementation with out-of-sample projection capability since typical software libraries do not support it. We provide an evaluation of the projection quality of five common DR algorithms (MDS, PCA, t-SNE, UMAP, and autoencoders) using quality metrics from the literature and analyze the trade-off between the size of the reference set and projection quality. The runtime behavior of the algorithms is also quantified with respect to reference set size, out-of-sample batch size, and dimensionality of the data sets. Furthermore, we compare the out-of-sample approach to other recently introduced DR methods, such as PaCMAP and TriMAP, which claim to handle larger data sets than traditional approaches. To showcase the usefulness of DR on this large scale, we contribute a use case where we analyze ensembles of streamlines amounting to one billion projected instances.

关键词： Dimensionality reduction out-of-core out-of-sample evaluation

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Fast Comparative analysis of Merge Trees Using Locality Sensitive Hashing

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ieee TRANSACTIONS ON visualization AND COMPUTER GRAPHICS 2025年第1期31卷 141-151页

作者： Lyu, Weiran Sridharamurthy, Raghavendra Phillips, Jeff M. Wang, Bei Univ Utah Salt Lake City UT 84112 USA

Scalar field comparison is a fundamental task in scientific visualization. In topological data analysis, we compare topological descriptors of scalar fields-such as persistence diagrams and merge trees-because they provide succinct and robust abstract representations. Several similarity measures for topological descriptors seem to be both asymptotically and practically efficient with polynomial time algorithms, but they do not scale well when handling large-scale, time-varying scientific data and ensembles. In this paper, we propose a new framework to facilitate the comparative analysis of merge trees, inspired by tools from locality sensitive hashing (LSH). LSH hashes similar objects into the same hash buckets with high probability. We propose two new similarity measures for merge trees that can be computed via LSH, using new extensions to Recursive MinHash and subpath signature, respectively. Our similarity measures are extremely efficient to compute and closely resemble the results of existing measures such as merge tree edit distance or geometric interleaving distance. Our experiments demonstrate the utility of our LSH framework in applications such as shape matching, clustering, key event detection, and ensemble summarization.

关键词： Merge trees locality sensitive hashing comparative analysis topological data analysis scientific visualization

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Sub-Linear Time Sampling Approach for large-scale data visualization Using Reinforcement Learning 13

Sub-Linear Time Sampling Approach for Large-Scale Data Visua...

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13th ieee symposium on large data analysis and visualization (LDAV)

作者： Biswas, Ayan Bhattacharya, Arindam Chen, Yi-Tang Shen, Han-Wei Los Alamos Natl Lab Los Alamos NM 87545 USA Uber Technol San Francisco CA USA Ohio State Univ Columbus OH 43210 USA

ISBN: (纸本)9798350330182

As the compute capabilities of the modern supercomputers continue to rise, domain scientists are able to run their simulations at very fine spatial and temporal resolutions. Compared to the compute speeds, the I/O bandwidth continues to lag by orders of magnitude. This necessitates that the analysis and data reduction are performed in situ while the simulation generated data is still at the supercomputer memory. Recently, intelligent data-driven sampling schemes have been proposed by visualization researchers. These sampling methods are scalable, in situ capable and able to identify the feature regions of the data. Although powerful, these sampling schemes need to traverse through the data sets at least twice, which can become problematic as the simulations start to touch the exascale capabilities. In this paper, we propose to use a reinforcement learning-based approach to devise a sub-linear time sampling algorithm. Using multi-arm bandits, we show that we can identify samples that are of similar or better quality compared to the existing methods while only touching a small fraction of the original data. We use multiple simulation data sets to show the efficacy of our proposed method.

关键词： big data data reduction multi-arm bandit reinforcement learning sampling visualization

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VisEval: A Benchmark for data visualization in the Era of large Language Models

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ieee TRANSACTIONS ON visualization AND COMPUTER GRAPHICS 2025年第1期31卷 1301-1311页

作者： Chen, Nan Zhang, Yuge Xu, Jiahang Ren, Kan Yang, Yuqing Microsoft Res Bengaluru India ShanghaiTech Univ Shanghai Peoples R China MoE Key Lab Intelligent Percept & Human Machine C Shanghai Peoples R China

Translating natural language to visualization (NL2VIS) has shown great promise for visual data analysis, 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-scale dataset. 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.

关键词： visualization evaluation automatic visualization large language models benchmark

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ENHANCING CHANGE DETECTION AND SUPER-RESOLUTION WITH NIMBO data

ENHANCING CHANGE DETECTION AND SUPER-RESOLUTION WITH NIMBO D...

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ieee International Geoscience and Remote Sensing symposium (IGARSS)

作者： Corpetti, Thomas Cusson, Thomas Lefebvre, Antoine Univ Rennes Univ Brest Nantes Univ CNRSUMR 6554 LETG F-35000 Rennes France Kermap 1137a Av Champs Blancs F-35510 Cesson Sevigne France

ISBN: (纸本)9798350360332;9798350360325

The Sentinel-2 (S2) satellite constellation offers new opportunities for monitoring the Earth. However, the raw data is difficult to use due to cloud cover and other limitations (10m resolution limited for analysis at fine scale, 16-bits representation that prevents from large scale visualization for example). As a consequence, several platforms have emerged to synthesize the data and offer easy-to-use products over the past years. Among them, NIMBO is a free, cloud-based platform that provides users with access to monthly images of the entire Earth. The platform uses deep neural networks to remove clouds from satellite images, colorizing images and providing users with a clear view of the Earth's surface. Providing homogeneous images makes it easier to develop large-scale tasks, in particular by creating reliable and robust datasets for various applications. This is illustrated in this paper, through two important applications: change detection and super-resolution.

关键词： Sentinel-2 large scale analysis NIMBO plateform change detection super-resolution

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Advancements in large-scale Image and Text Representation Learning: A Comprehensive Review and Outlook

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ieee ACCESS 2025年 13卷 49922-49933页

作者： Qin, Yang Ding, Shuxue Xie, Huiming Guilin Univ Elect Technol Guangxi Coll & Univ Key Lab AI Algorithm Engn Sch Artificial Intelligence Guilin 541004 Peoples R China Guilin Univ Aerosp Technol Engn Comprehens Training Ctr Guilin 541004 Peoples R China

large-scale image and text representation learning is critical in determining the performance of multimodal tasks involving images and text, such as visual question answering and image captioning. Most existing research on large-scale image and text representation learning relies on Transformer networks for pre-training, i.e., learning generic semantic representations from large-scale image-to-text pairs. These representations are then fine-tuned and transferred to downstream multimodal tasks. This paper first provides a brief analysis of the advantages of pre-training models. It then comprehensively summarizes the relevant research on large-scale image and text representation learning based on pre-training. The focus is on pre-training model architectures, pre-training tasks, and image-text datasets. Finally, we provide a summary and outlook of large-scale image and text representation learning.

关键词： Transformers Image coding Representation learning Feature extraction Encoding Vectors data models visualization Analytical models Semantics Image-and-text large-scale representation learning pre-training transformer self-supervised learning

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Visual Exploratory analysis for Designing large-scale Network-on-Chip Architectures: A Domain Expert-Led Design Study

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ieee TRANSACTIONS ON visualization AND COMPUTER GRAPHICS 2024年第4期30卷 1970-1983页

作者： Wang, Shaoyu Yan, Hang Isaacs, Katherine E. Sun, Yifan Huazhong Univ Sci & Technol Wuhan 430074 Peoples R China Univ Utah Salt Lake City UT 84112 USA William & Mary Williamsburg VA 23185 USA

visualization design studies bring together visualization researchers and domain experts to address yet unsolved data analysis challenges stemming from the needs of the domain experts. Typically, the visualization researchers lead the design study process and implementation of any visualization solutions. This setup leverages the visualization researchers' knowledge of methodology, design, and programming, but the availability to synchronize with the domain experts can hamper the design process. We consider an alternative setup where the domain experts take the lead in the design study, supported by the visualization experts. In this study, the domain experts are computer architecture experts who simulate and analyze novel computer chip designs. These chips rely on a Network-on-Chip (NOC) to connect components. The experts want to understand how the chip designs perform and what in the design led to their performance. To aid this analysis, we develop Vis4Mesh, a visualization system that provides spatial, temporal, and architectural context to simulated NOC behavior. Integration with an existing computer architecture visualization tool enables architects to perform deep-dives into specific architecture component behavior. We validate Vis4Mesh through a case study and a user study with computer architecture researchers. We reflect on our design and process, discussing advantages, disadvantages, and guidance for engaging in a domain expert-led design studies.

关键词： data visualization Task analysis Computer architecture Network-on-chip Behavioral sciences Synchronization Collaboration design study network-on-chip performance analysis

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Immersive Analytics as a Support Medium for data-Driven Monitoring in Hydropower

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ieee TRANSACTIONS ON visualization AND COMPUTER GRAPHICS 2025年第5期31卷 3536-3546页

作者： Medeiros, Marina Lima Kaufmann, Hannes Schmidt, Johanna VRVis GmbH Vienna Austria TU Wien Vienna Austria

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.

关键词： data visualization Three-dimensional displays Spatial databases Hydroelectric power generation data analysis Analytical models Solid modeling Sensors Prototypes Time series analysis data visualization Digitaization Spatial data Abstract data Egocentric navigation

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