View-Dependent Visualization for Analysis of Large Datasets

Due to the impressive capabilities of human visual processing, interactive visualization methods have become essential tools for scientists to explore and analyze large, complex datasets. However, traditional approaches do not account for the increased size or latency of data retrieval when interacting with these often remote datasets. In this dissertation, I discuss two novel design paradigms, based on accepted models of the information visualization process and graphics hardware pipeline, that are appropriate for interactive visualization of large remote datasets. In particular, I discuss novel solutions aimed at improving the performance of interactive visualization systems when working with large numeric datasets and large terrain (elevation and imagery) datasets by using data reduction and asynchronous retrieval of view-prioritized data, respectively. First I present a modified version of the standard information visualization model that accounts for the challenges presented by interacting with large, remote datasets. I also provide the details of a software framework implemented using this model and discuss several different visualization applications developed within this framework. Next I present a novel technique for leveraging the hardware graphics pipeline to provide asynchronous, view-prioritized data retrieval to support interactive visualization of remote terrain data. I provide the results of statistical analysis of performance metrics to demonstrate the effectiveness of this approach. Finally I present the details of two novel visualization techniques, and the results of evaluating these systems using controlled user studies and expert evaluation. The results of these qualitative and quantitative evaluation mechanisms demonstrate improved visual analysis task performance for large numeric datasets

Inverting the model of genomics data sharing with the NHGRI Genomic Data Science Analysis, Visualization, and Informatics Lab-space

The NHGRI Genomic Data Science Analysis, Visualization, and Informatics Lab-space (AnVIL; https://anvilproject.org) was developed to address a widespread community need for a unified computing environment for genomics data storage, management, and analysis. In this perspective, we present AnVIL, describe its ecosystem and interoperability with other platforms, and highlight how this platform and associated initiatives contribute to improved genomic data sharing efforts. The AnVIL is a federated cloud platform designed to manage and store genomics and related data, enable population-scale analysis, and facilitate collaboration through the sharing of data, code, and analysis results. By inverting the traditional model of data sharing, the AnVIL eliminates the need for data movement while also adding security measures for active threat detection and monitoring and provides scalable, shared computing resources for any researcher. We describe the core data management and analysis components of the AnVIL, which currently consists of Terra, Gen3, Galaxy, RStudio/Bioconductor, Dockstore, and Jupyter, and describe several flagship genomics datasets available within the AnVIL. We continue to extend and innovate the AnVIL ecosystem by implementing new capabilities, including mechanisms for interoperability and responsible data sharing, while streamlining access management. The AnVIL opens many new opportunities for analysis, collaboration, and data sharing that are needed to drive research and to make discoveries through the joint analysis of hundreds of thousands to millions of genomes along with associated clinical and molecular data types

Interactive physically-based cloud simulation

Due to the character of the original source materials and the nature of batch digitization, quality control issues may be present in this document. Please report any quality issues you encounter to [email protected], referencing the URI of the item.Includes bibliographical references (leaves 60-62).Issued also on microfiche from Lange Micrographics.Clouds play an important role in the depiction of many natural outdoor scenes. Realistic modeling and rendering of such scenes is important for applications in games, military training simulations, flight simulations, and even in the creation of digital artistic media. Previous methods for modeling the growth of clouds do not account for the fluid interactions that are responsible for cloud formation in the physical atmosphere. We propose a model for simulating cloud formation based on a basic computational fluid solver. This allows us to simulate the complex air motion that contributes to cloud formation in our atmosphere. Among the natural processes that we simulate are buoyancy, relative humidity, and condensation. Because we have built this model on top of a visually realistic fluid simulator with which the user is able to interact, we are also able to give the user artistic control over a physically accurate environment. In practice, the user need only set the initial conditions for our virtual atmosphere to produce different types of cloud formations

Interactive physically-based cloud simulation

Clouds play an important role in the depiction of many natural outdoor scenes. Realistic modeling and rendering of such scenes is important for applications in games, military training simulations, flight simulations, and even in the creation of digital artistic media. Previous methods for modeling the growth of clouds do not account for the fluid interactions that are responsible for cloud formation in the physical atmosphere. We propose a model for simulating cloud formation based on a basic computational fluid solver. This allows us to simulate the complex air motion that contributes to cloud formation in our atmosphere. Among the natural processes that we simulate are buoyancy, relative humidity, and condensation. Because we have built this model on top of a visually realistic fluid simulator with which the user is able to interact, we are also able to give the user artistic control over a physically accurate environment. In practice, the user need only set the initial conditions for our virtual atmosphere to produce different types of cloud formations. iii For us iv ACKNOWLEDGEMENTS I would like to thank everyone who has contributed to this project, especially my colleague and partner in research, Zeki Melek, who has been around to help answer countless questions. I would also like to thank my advisor, John Keyser, for guiding my vision and helping me to get my work published. I must also thank Dr. Mike Biggerstaf