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International Research and Training Group 1131 Workshop on visualization of Large and Unstructured data Sets: Applications in Geospatial Planning, Modeling and Engineering, VLUDS 2011

作者： Engel, Daniel Hüttenberger, Lars Hamann, Bernd Computer Graphics and HCI Group University of Kaiserslautern Germany CS Department Institute for Data Analysis and Visualization University of California Davis United States

ISBN: (纸本)9783939897460

Dimension reduction is commonly defined as the process of mapping high-dimensional data to a lower-dimensional embedding. Applications of dimension reduction include, but are not limited to, filtering, compression, regression, classification, feature analysis, and visualization. We review methods that compute a point-based visual representation of high-dimensional data sets to aid in exploratory data analysis. The aim is not to be exhaustive but to provide an overview of basic approaches, as well as to review select state-of-the-art methods. Our survey paper is an introduction to dimension reduction from a visualization point of view. Subsequently, a comparison of state-of-the-art methods outlines relations and shared research foci.

关键词： visualization

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Extreme data Management analysis and visualization:Exploiting Large data for Science Discovery

Extreme Data Management Analysis and Visualization:Exploitin...

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2014第三届国际新兴信息及通讯技术产业大会

作者： Valerio Pascucci Center for Extreme Data Management Analysis and Visualization (CEDMAV) University of Utah

Effective use of data management techniques for massive scientific data is a crucial ingredient for the success of any supercomputing center and data intensive scientific investigation. Developing such techniques involves a number of major challenges such as the real-time management of massive data, or the quantitative analysis of scientific features of unprecedented complexity. The Center for Extreme data Management analysis and visualization(CEDMAV) addresses these challenges with and interdisciplinary research in diverse topics including the mathematical foundations of data representations, the design of robust, efficient algorithms, and the integration with relevant applications in physics, biology, or medicine. In this talk, I will discuss one approach developed for dealing with massive amount of information via a framework for processing large scale scientific data with high performance selective queries on multiple terabytes of raw data. The combination of this data model with progressive streaming techniques allows achieving interactive processing rates on a variety of computing devices ranging from handheld devices like an i Phone, to simple workstations,to the I/O of parallel supercomputers. With this framework we demonstrated how one can enable the real time streaming of massive combustion simulations from DOE platforms such as Hopper2 at LBNL and Intrepid at ANL. I will also present the application of a discrete topological framework for the representation and analysis of the same large scale scientific data. Due to the combinatorial nature of this framework, we can implement the core constructs of Morse theory without the approximations and instabilities of classical numerical techniques. The inherent robustness of the combinatorial algorithms allows us to address the high complexity of the feature extraction problem for high resolution scientific data. During the talk, I will provide a live demonstration of the effectiveness of some software tools deve

关键词： Extreme data Management analysis and visualization:Exploiting Large data for Science Discovery

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Immersive visualization for materials science data analysis using the Oculus Rift

Immersive visualization for materials science data analysis ...

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IEEE International Conference on Big data

作者： Margaret Drouhard Chad A. Steed Steven Hahn Thomas Proffen Jamison Daniel Michael Matheson Computational Sciences and Engineering Division Oak Ridge National Laboratory Oak Ridge TN Neutron Data Analysis and Visualization Division Oak Ridge National Laboratory Oak Ridge TN National Center for Computational Sciences Oak Ridge National Laboratory Oak Ridge TN

In this paper, we propose strategies and objectives for immersive data visualization with applications in materials science using the Oculus Rift virtual reality headset. We provide background on currently available analysis tools for neutron scattering data and other large-scale materials science projects. In the context of the current challenges facing scientists, we discuss immersive virtual reality visualization as a potentially powerful solution. We introduce a prototype immersive visualization system, developed in conjunction with materials scientists at the Spallation Neutron Source, which we have used to explore large crystal structures and neutron scattering data. Finally, we offer our perspective on the greatest challenges that must be addressed to build effective and intuitive virtual reality analysis tools that will be useful for scientists in a wide range of fields.

关键词： data visualization Three-dimensional displays Neutrons Solid modeling Crystals Virtual reality Instruments

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PraK Tool V3: Enhancing Video Item Search Using Localized Text and Texture Queries 31st

PraK Tool V3: Enhancing Video Item Search Using Localized Te...

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31st International Conference on Multimedia Modeling, MMM 2025

作者： Stroh, Michael Kloda, Vojtěch Verner, Benjamin Vopálková, Zuzana Buchmüller, Raphael Jäckl, Bastian Hajko, Jakub Lokoč, Jakub Visual Computing Department of Computer Science Faculty of Natural Sciences Konstanz University Konstanz Germany SIRET Research Group Department of Software Engineering Faculty of Mathematics and Physics Charles University Prague Czech Republic Data Analysis and Visualization Department of Computer Science Faculty of Natural Sciences Konstanz University Konstanz Germany

ISBN: (纸本)9789819620739

We present a third version of the PraK system designed around an effective text-image and image-image search model. The system integrates sub-image search options for localized context search for CLIP and image color/texture and interfaces tailored to specific task types. A user study shows the benefits of sub-image search in homogeneous video collections. We further enhance the user experience of the PraK framework by re-designing the frontend and introducing task-specific views. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Video analysis

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Enhancing medical vision-language contrastive learning via inter-matching relation modelling

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IEEE Transactions on Medical Imaging 2025年 PP卷 PP页

作者： Li, Mingjian Meng, Mingyuan Fulham, Michael Feng, David Dagan Bi, Lei Kim, Jinman University of Sydney Biomedical data analysis and visualization lab School of Computer Science NSW Australia Royal Prince Alfred Hospital Department of Molecular Imaging NSW Australia Shanghai Jiao Tong University Institute of Translational Medicine Shanghai China

Medical image representations can be learned through medical vision-language contrastive learning (mVLCL) where medical imaging reports are used as weak supervision through image-text alignment. These learned image representations can be transferred to and benefit various downstream medical vision tasks such as disease classification and segmentation. Recent mVLCL methods attempt to align image sub-regions and the report keywords as local-matchings. However, these methods aggregate all local-matchings via simple pooling operations while ignoring the inherent relations between them. These methods therefore fail to reason between local-matchings that are semantically related, e.g., local-matchings that correspond to the disease word and the location word (semantic-relations), and also fail to differentiate such clinically important local-matchings from others that correspond to less meaningful words, e.g., conjunction words (importance-relations). Hence, we propose a mVLCL method that models the inter-matching relations between local-matchings via a relation-enhanced contrastive learning framework (RECLF). In RECLF, we introduce a semantic-relation reasoning module (SRM) and an importance-relation reasoning module (IRM) to enable more fine-grained report supervision for image representation learning. We evaluated our method using six public benchmark datasets on four downstream tasks, including segmentation, zero-shot classification, linear classification, and cross-modal retrieval. Our results demonstrated the superiority of our RECLF over the state-of-the-art mVLCL methods with consistent improvements across single-modal and cross-modal tasks. These results suggest that our RECLF, by modelling the inter-matching relations, can learn improved medical image representations with better generalization capabilities. © 2025 IEEE.

关键词： Contrastive Learning

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Detecting Oil Palm Plantations and Mangroves in Se Asia Among Igbp Land Cover Units Mapped by the Modis

SSRN

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SSRN 2025年

作者： Ye Myint, Yadanar Percival, Joseph Emile Honour Kitajima, Kaoru Division of Forest and Biomaterials Sciences Graduate School of Agriculture Kyoto University Kyoto Japan Spatial Data Analysis and Visualization Lab University of Hawaii at Hilo Hilo WI96720-4091 United States Department of Geography & Environmental Science University of Hawai‘i at Hilo HiloHI96720 United States

Moderate Resolution Imaging Spectroradiometer (MODIS) Land Cover type product (MODIS-LC) is widely used for global land cover classification to follow the International Geosphere-Biosphere Programme (IGBP) scheme. The geographical dimension of 1200 km x 1200 km is appropriate for regional and national level tracking of deforestation in SE Asia (4.5 million ha), where oil palm plantations are a key driver of forest loss in SE Asia. Here, we examined forest conversion to other land cover types in eight SE Asian countries during two periods (2001-2010 and 2010-2018) using the MODIS MCD12Q1 data in ArcMap 10.8.2. The results showed that 12% and 9% of forested areas were converted to woody savanna and savanna in 2001-2010 and 2010-2018, respectively. Then, we selected 260 random subsamples (500 m polygons) from each of the common land cover types in SE Asia and verified or reclassified their vegetation with visual examination of high-resolution Google Earth imagery. However, verification with high-resolution images reduced these estimates to 5.1 % and 4.1 %, because 44% and 46% of areas initially classified as woody savannas and savannas were actually oil palms. Using calibration factor for each vegetation type, 47.2 % and 47.9 % of forest losses were estimated to be attributable to conversion to oil palm plantations, whereas the remaining forest loss was attributed to degradation to savannas and grassland. In addition, the IGBP land cover type of permanent wetland was found to consist of mangroves (63%), aqua farms (20%), and oil palms (8%) rather than permanent wetland per se. While MODIS-LC is a powerful dataset for global analyses, regional calibration with high-resolution imagery, as demonstrated in this study, provides insight that can better inform policy makers. © 2025, The Authors. All rights reserved.

关键词： Deforestation

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Discovering parametric clusters in social small-world graphs 05

Discovering parametric clusters in social small-world graphs

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20th Annual ACM Symposium on Applied Computing

作者： McPherson, Jonathan Ma, Kwan-Liu Ogawa, Michael Institute for Data Analysis and Visualization University of California Davis

ISBN: (纸本)9781581139648

We present a strategy for analyzing large, social small-world graphs, such as those formed by human networks. Our approach brings together ideas from a number of different research areas, including graph layout, graph clustering and partitioning, machine learning, and user interface design. It helps users explore the networks and develop insights concerning their members and structure that may be difficult or impossible to discover via traditional means, including existing graph visualization and/or statistical methods. Copyright 2005 ACM.

关键词： Graph theory

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Visual analytics of terrorist activities related to epidemics

Visual analytics of terrorist activities related to epidemic...

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2nd IEEE Conference on Visual Analytics Science and Technology 2011, VAST 2011

作者： Bertini, Enrico Buchmuller, Juri Fischer, Fabian Huber, Stephan Lindemeier, Thomas Maass, Fabian Mansmann, Florian Ramm, Thomas Regenscheit, Michael Rohrdantz, Christian Scheible, Christian Schreck, Tobias Sellien, Stephan Stoffel, Florian Tautzenberger, Mark Zieker, Matthias Keim, Daniel A. Data Analysis and Visualization Group University of Konstanz Germany

ISBN: (纸本)9781467300131

The task of the VAST 2011 Grand Challenge was to investigate potential terrorist activities and their relation to the spread of an epidemic. Three different data sets were provided as part of three Mini Challenges (MCs). MC 1 was about analyzing geo-tagged microblogging (Twitter) messages to characterize the spread of an epidemic. MC 2 required analyzing threats to a computer network using a situational awareness approach. In MC 3 possible criminal and terrorist activities were to be analyzed based on a collection of news articles. To solve the Grand Challenge, insight from each of the individual MCs had to be integrated appropriately. © 2011 IEEE.

关键词： Epidemiology

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OCP-Operational Curricular Planning: A Visual Decision Support System for Planning Teaching Resources at Universities

OCP-Operational Curricular Planning: A Visual Decision Suppo...

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2018 International Symposium on Big data Visual and Immersive Analytics, BDVA 2018

作者： Sahann, Raphael Möller, Torsten Visualization and Data Analysis University of Vienna Vienna Austria

ISBN: (纸本)9781538691946

We conducted a design study to do an in-depth analysis of the problem of operational planning at universities and designed a decision support tool for that problem, called Operational Curricular Planning (OCP). Based on our observations we abstracted the planning process into separate tasks. Focusing on a subset of tasks that we characterized, we present the OCP tool for visually supporting decision making in the process of planning teaching resources. We show the steps leading to the final design of our visual decision support system and discuss the design decisions made while building the tool. Finally, we present an evaluation with four domain experts in a real- world scenario and talk about lessons learned from building the OCP tool, including the issue of integration and adoption of the system. © 2018 IEEE.

关键词： Decision support systems

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G-Rap: Interactive text synthesis using recurrent neural network suggestions 26

G-Rap: Interactive text synthesis using recurrent neural net...

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26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2018

作者： Schlegel, Udo Cakmak, Eren Buchmüller, Juri Keim, Daniel A. University of Konstanz Data Analysis and Visualization Group Konstanz Germany

ISBN: (纸本)9782875870476

Finding the best neural network configuration for a given goal can be challenging, especially when it is not possible to assess the output quality of a network automatically. We present G-Rap, an interactive interface based on Visual Analytics principles for comparing outputs of multiple RNNs for the same training data. G-Rap enables an iterative result generation process that allows a user to evaluate the outputs with contextual statistics. © ESANN 2018 - Proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning.

关键词： Recurrent neural networks

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