Remote interactive visualization for particle-based simulations on graphics clusters

Scientific visualization allows researchers to examine their data in high definition 2D and 3D environments. Field research conducted in the Middle East by the UC San Diego Levantine and Cyber-Archaeology Lab, in collaboration with the Center for Interdisciplinary Research in Art, Architecture and Archaeology (CISA3) takes full advantage of a suite of state-of-the-art visualization platforms at the UC San Diego Qualcomm Institute. Research interests include terascale scientific visualization and virtual reality, image-based modeling and rendering, as well as distributed and remote visualization. The Qualcomm Institute's team has been active in virtual reality research for over a decade and the stringent VR requirements have served as important performance criteria for our large-scale distributed data analysis and visualization projects. Through collaborative work both on campus and abroad, our group has contributed to making a significant transformative impact to both diagnostics and the preservation of cultural heritage. Here we briefly summarize a range of 3D applications using data from our research in Jordan and Saudi Arabia.

Intuitive and efficient interaction techniques are a fundamental component of most visualization tools. The integration of interaction techniques into visual representations (and automatic analysis methods in visual analytics) supports the human-information discourse and can be realized in various ways. But we also have to take care that our interaction and visual representation techniques are validated in order to get a clear understanding of their efficiency and usability. In this talk, I will explore current and identify future trends in the development of novel interaction and evaluation techniques for information visualization and related fields. Here, I especially want to highlight recent findings in the use of brain-computer interfaces to adapt and evaluate visualizations.

This paper focuses on interactive data visualization techniques and their applications in healthcare systems. Interactive data visualization is a collection of techniques translating data from its numeric format to graphic presentation dynamically for easy understanding and visual impact. Compared to conventional static data visualization techniques, interactive data visualization techniques allow users to self-explore the entire data set by instant slice and dice, quick switching among multiple data sources. Adjustable granularity of interactive data visualization allows for both detailed micro information and aggregated macro information displayed in a single chart. Animated transition adds extra visual impact that describes how system transits from one state to another. When applied to healthcare system, interactive visualization techniques are useful in areas such as information integration, flow or trajectory presentation and location related visualization, etc. In this paper, three case studies are shared to illustrate how interactive data visualization techniques are applied to various aspects of healthcare systems. The first case study shows a pathway visualization representing longitudinal disease progression of a patient cohort. The second case study shows a dashboard profiling different patient cohorts from multiple perspectives. The third case study shows an interactive map illustrating patient geographical distribution at adjustable granularity. All three case studies illustrate that interactive data visualization techniques help quick information access, fast knowledge sharing and better decision making in healthcare system.

Several Visualization techniques have been applied in traffic information area. This work is the basic research for applying of interactive visualization techniques when the traffic information is provided to users. Design of interactive visualization techniques were applied to traffic information services. Previous studies for information visualization with interactive visualization techniques were investigated, and the traffic information services currently provided were analyzed. There are many types of interactive visualization techniques, but this study is mainly focused on selecting, querying, linking, filtering and rearranging techniques. The users can obtain the proper and more suitable information for theie needs, because each interactive visualization techniques support interaction between information and users. The traffic information which has one of interactive techniques can be more effective for decision making and utilization. More attention is given to interactive visualization of data and information techniques in transportation field. By this research, it is expected that traffic information services is more effective and can be a foundation work for various interactive visualization techniques in traffice inforamtion.

Based on the current status of forest visual simulation and tree growth-oriented management theory, and referring to fir stand growth and management process interact, the paper analysed the stand management process and growth interact process, combining with stand growth modal, the paper designed a visual simulation approach of management and stand growth interact feedback. By using the stand growth result and stand management measurement as interface parameter, analysing process defined management plan, by real-time scheduling algorithm parsing the management process, and by using management processes and stand growth dynamic visualization to get the final result of stimulation management result. Sub-fir growth and management process simulation for interactive visualization can provide a scientific basis for management planning and decision-making.

ABSTRACT: Many companies today utilize interactive data visualization to present accounting information to external users on their investor relations websites and to internal users in applications such as enterprise resource planning, Balanced Scorecard, network security, and fraud detection systems. We develop a taxonomy for examining the current state of interactive data visualization research related to accounting decision making. We organize our review around three themes: the relationship between task characteristics and interactive data visualization techniques, the relationship between decision maker characteristics and interactive data visualization techniques, and the impact of interactive data visualization techniques on decision processes and outcomes. The review categorizes relevant research, describes the research questions addressed, and suggests avenues for further research.

This article presents a platform for interactive graph mining and relational machine learning called GraphVis. The platform combines interactive visual representations with state-of-the-art graph mining and relational machine learning techniques to aid in revealing important insights quickly as well as learning an appropriate and highly predictive model for a particular task (e.g., classification, link prediction, discovering the roles of nodes, and finding influential nodes). Visual representations and interaction techniques and tools are developed for simple, fast, and intuitive real-time interactive exploration, mining, and modeling of graph data. In particular, we propose techniques for interactive relational learning (e.g., node/link classification), interactive link prediction and weighting, role discovery and community detection, higher-order network analysis (via graphlets, network motifs), among others. GraphVis also allows for the refinement and tuning of graph mining and relational learning methods for specific application domains and constraints via an end-to-end interactive visual analytic pipeline that learns, infers, and provides rapid interactive visualization with immediate feedback at each change/prediction in real-time. Other key aspects include interactive filtering, querying, ranking, manipulating, exporting, as well as tools for dynamic network analysis and visualization, interactive graph generators (including new block model approaches), and a variety of multi-level network analysis techniques.

The paper provides an overview of an experimental, Grid‐like computing environment, Polder, and its components. Polder offers high‐performance computing and interactive simulation facilities to computational science. It was successfully implemented on a wide‐area cluster system, the Distributed ASCI Supercomputer. An important issue is an efficient management of resources, in particular multi‐level scheduling and migration of tasks that use PVM or sockets. The system can be applied to interactive simulation, where a cluster is used for high‐performance computations, while a dedicated immersive interactive environment (CAVE) offers visualization and user interaction. Design considerations for the construction of dynamic exploration environments using such a system are discussed, in particular the use of intelligent agents for coordination. A case study of simulatedabdominal vascular reconstruction is subsequently presented: the results of computed tomography or magnetic resonance imaging of a patient are displayed in CAVE, and a surgeon can evaluate the possible treatments by performing the surgeries virtually and analysing the resulting blood flow which is simulated using the lattice‐Boltzmann method. Copyright © 2002 John Wiley & Sons, Ltd.

Visual simulation of natural erosion on terrains has always been a fascinating research topic in the field of computer graphics. While there are many algorithms already developed to improve the visual quality of terrain, the recent simulation methods revolve around physically-based hydraulic erosion because it can generate realistic natural-looking terrains. However, many of such algorithms were tested only on low resolution terrains. When simulated on a higher resolution terrain, most of the current algorithms become computationally expensive. This is why in many applications today, terrains are generated off-line and loaded during the application runtime. This method restricts the number of terrains which can be stored if there is a limitation on storage capacity.Recently, graphics hardware has evolved into an indispensable tool in improving the speed of computation. This has motivated us to develop an erosion algorithm to map to graphics hardware for faster terrain generation.In this paper, we propose a fast and efficient hydraulic erosion procedural technique that utilizes the GPUs powerful computation capability in order to generate high resolution erosion on terrains. Our method is based on the Newtonian physics approach that is implemented on a two-dimensional data structure which stores height fields, water amount, and dissolved sediment and water velocities. We also present a comprehensive comparison between the CPU and GPU implementations together with the visual results and the statistics on simulation time taken.

Slow-moving inventory (SMI), which absorbs working capital over long periods of time and pushes up storage cost, urgently requires reliable and intelligible forecasting methods for making business decisions. We suggest the use of the Temporal Fusion Transformer as the backbone to fuse the graph attention layer, to capture the substitution effect and promotion transmission between SKUs. Secondly, multi-scale expansion causal CNNs account for both long-term and short-term seasonal patterns while Bayesian residual branches measure the uncertainty of prediction. Attention-based feature selectors are designed in the training stage, while SHAP interpretation and counterfactual inference are integrated in the inference stage to interpret how price, demand, and logistics signals contribute to SMI prediction. All the results are integrated into the adaptive control chart of the interactive visual display of feature attribution heat map, forecast interval and core KPI Inventory Turnover in real time, and automatically launch early warning and hypothesis testing and scene simulation when anomalies are detected, to help managers to judge whether to advance the replenishment strategy or clearance strategy, to achieve the closed loop of forecasting and decision. Simulations conducted by a multinational consumer electronics retailer showed an increase in inventory turnover of approximately 14.6%.

Evaluating ice loads acting on ships is essential for the safety of ships navigating in ice-covered seas. In this study, we develop a CFD method to handle ship, ice, and fluid interaction. The lattice Boltzmann method, capable of large-scale calculations, is applied to the simulation of free-surface fluids. The ice motion is computed by solving the equations of motion of a rigid body, and the discrete element method models the ice-ice and ice-ship contact interactions. A momentum exchange scheme couples the lattice Boltzmann method and particle-based rigid body simulation. We introduce tree-based adaptive mesh refinement and multiple GPU computing to improve grid resolution and computational time. The proposed method is applied to model scale simulations of ship navigation in a brash ice channel. Simulations were performed for various conditions with different ice concentrations and ship velocities, and we observed that ice resistance increased with the ice concentration and the ship velocity increased. The ice motions and resistances obtained from our simulations are reasonable compared to model equations of Finnish-Swedish ice class rules (FSICR) and numerical analyses of a previous study.

A chorem-based approach for visually analyzing spatial data

We propose an algorithm for fluid simulation in three-dimensional scenes with obstacles represented as implicit surfaces. The fluid simulation is performed based on the smoothed particle hydrodynamics method and the behavior of fluid particles in the vicinity of obstacles is defined by introducing a new model of particle motion specific to implicitly representation. For effective parallelization on the graphics processing units, we apply the grid of polynomial method to express the implicitly defined obstacles. The proposed method takes advantage of the properties of implicit representation such as smoothness, shape modelling and the expression of deforming objects.

Visual simulation allowed us to show the managers of a small clothing plant how the Kanban method could help them, to persuade them to accept it, and to support its implementation. A nonvisual simulation on a minicomputer did not convince managers that the potential benefits of the Kanban method could be obtained in the shop. A series of three visual simulations on microcomputers effectively demonstrated the method and its performance advantages. Managers better understood the nature of the model and its workings. As a result, a pilot project was set up, leading in turn to the adoption of the method in the plant.

Dimensionality reduction is a common method for analyzing and visualizing high-dimensional data. However, reasoning dynamically about the results of a dimensionality reduction is difficult. Dimensionality-reduction algorithms use complex optimizations to reduce the number of dimensions of a dataset, but these new dimensions often lack a clear relation to the initial data dimensions, thus making them difficult to interpret. Here we propose a visual interaction framework to improve dimensionality-reduction based exploratory data analysis. We introduce two interaction techniques, forward projection and backward projection, for dynamically reasoning about dimensionally reduced data. We also contribute two visualization techniques, prolines and feasibility maps, to facilitate the effective use of the proposed interactions. We apply our framework to PCA and autoencoder-based dimensionality reductions. Through data-exploration examples, we demonstrate how our visual interactions can improve the use of dimensionality reduction in exploratory data analysis.