Facilitating insight into a simulation model using visualization and dynamic model previews

This paper shows how model simplification, by replacing iterative steps with unitary predictive equations, can enable dynamic interaction with a complex simulation process. Model previews extend the techniques of dynamic querying and query previews into the context of ad hoc simulation model exploration. A case study is presented within the domain of counter-current chromatography. The relatively novel method of insight evaluation was applied, given the exploratory nature of the task. The evaluation data show that the trade-off in accuracy is far outweighed by benefits of dynamic interaction. The number of insights gained using the enhanced interactive version of the computer model was more than six times higher than the number of insights gained using the basic version of the model. There was also a trend for dynamic interaction to facilitate insights of greater domain importance

Void-and-Cluster Sampling of Large Scattered Data and Trajectories

We propose a data reduction technique for scattered data based on statistical sampling. Our void-and-cluster sampling technique finds a representative subset that is optimally distributed in the spatial domain with respect to the blue noise property. In addition, it can adapt to a given density function, which we use to sample regions of high complexity in the multivariate value domain more densely. Moreover, our sampling technique implicitly defines an ordering on the samples that enables progressive data loading and a continuous level-of-detail representation. We extend our technique to sample time-dependent trajectories, for example pathlines in a time interval, using an efficient and iterative approach. Furthermore, we introduce a local and continuous error measure to quantify how well a set of samples represents the original dataset. We apply this error measure during sampling to guide the number of samples that are taken. Finally, we use this error measure and other quantities to evaluate the quality, performance, and scalability of our algorithm.Comment: To appear in IEEE Transactions on Visualization and Computer Graphics as a special issue from the proceedings of VIS 201

Hillview:A trillion-cell spreadsheet for big data

Hillview is a distributed spreadsheet for browsing very large datasets that cannot be handled by a single machine. As a spreadsheet, Hillview provides a high degree of interactivity that permits data analysts to explore information quickly along many dimensions while switching visualizations on a whim. To provide the required responsiveness, Hillview introduces visualization sketches, or vizketches, as a simple idea to produce compact data visualizations. Vizketches combine algorithmic techniques for data summarization with computer graphics principles for efficient rendering. While simple, vizketches are effective at scaling the spreadsheet by parallelizing computation, reducing communication, providing progressive visualizations, and offering precise accuracy guarantees. Using Hillview running on eight servers, we can navigate and visualize datasets of tens of billions of rows and trillions of cells, much beyond the published capabilities of competing systems

3D Visualization Modules for Chemical Engineering – A Web-Based Approach Using Java and OpenGL

The main objective of this work is to implement web-based educational modules for chemical engineering students. Phase behavior is a topic with which the students seem to struggle with, particularly for mixtures, where a 2-D representation of the phase diagram falls far short of the understanding a 3-D model can provide. Using the platform-independence of Java and the graphics capability of OpenGL, three phase diagram Java applets have been developed. Users can view these web-based 3D applets by installing a plug-in. These modules provide users with an ability to rotate the 3D models, slice through them, zoom into them and view their various 2D projections. Also, a molecular simulation applet for measuring chemical potential of binary mixtures has been developed, using a Java-based molecular simulation application-programming interface (API). First, the thesis presents a brief overview of phase diagrams and explains why modeling them using computer graphics is useful. While visualization involves the merging of data with the display of geometric objects through computer graphics, it is important to study the software issues involved in web-based visualization. The paper explains the visualization framework by describing the visualization pipeline and then using it as a guideline for the development of the modules. Next, the paper describes the development of the molecular simulation applet using a molecular simulation API - Etomica. The Java applet provides for dynamic modification and interrogation of the simulation, while it is in progress, which enables students to see directly the effect of changing state conditions or molecular interactions on the behavior of the molecules and on the outcome of the simulation. It is hoped that by using these web-based 3D phase diagrams the chemical engineering students would gain a better understanding of the complicated 3D models, making this package a useful instructional aid. It is also hoped that the molecular simulation applet would be an effective tool to help students understand molecular simulations

An Enhanced Visualization Process Model for Incremental Visualization

Abstract-With today's technical possibilities, a stable visualization scenario can no longer be assumed as a matter of course, as underlying data and targeted display setup are much more in flux than in traditional scenarios. Incremental visualization approaches are a means to address this challenge, as they permit the user to interact with, steer, and change the visualization at intermediate time points and not just after it has been completed. In this paper, we put forward a model for incremental visualizations that is based on the established Data State Reference Model, but extends it in ways to also represent partitioned data and visualization operators to facilitate intermediate visualization updates. In combination, partitioned data and operators can be used independently and in combination to strike tailored compromises between output quality, shown data quantity, and responsiveness-i.e., frame rates. We showcase the new expressive power of this model by discussing the opportunities and challenges of incremental visualization in general and its usage in a real world scenario in particular

A Fast and Scalable System to Visualize Contour Gradient from Spatio-temporal Data

Changes in geological processes that span over the years may often go unnoticed due to their inherent noise and variability. Natural phenomena such as riverbank erosion, and climate change in general, is invisible to humans unless appropriate measures are taken to analyze the underlying data. Visualization helps geological sciences to generate scientific insights into such long-term geological events. Commonly used approaches such as side-by-side contour plots and spaghetti plots do not provide a clear idea about the historical spatial trends. To overcome this challenge, we propose an image-gradient based approach called ContourDiff. ContourDiff overlays gradient vector over contour plots to analyze the trends of change across spatial regions and temporal domain. Our approach first aggregates for each location, its value differences from the neighboring points over the temporal domain, and then creates a vector field representing the prominent changes. Finally, it overlays the vectors (differential trends) along the contour paths, revealing the differential trends that the contour lines (isolines) experienced over time. We designed an interface, where users can interact with the generated visualization to reveal changes and trends in geospatial data. We evaluated our system using real-life datasets, consisting of millions of data points, where the visualizations were generated in less than a minute in a single-threaded execution. We show the potential of the system in detecting subtle changes from almost identical images, describe implementation challenges, speed-up techniques, and scope for improvements. Our experimental results reveal that ContourDiff can reliably visualize the differential trends, and provide a new way to explore the change pattern in spatiotemporal data. The expert evaluation of our system using real-life WRF (Weather Research and Forecasting) model output reveals the potential of our technique to generate useful insights on the spatio-temporal trends of geospatial variables