Luke J. Gosink

Variable Interactions in Query-Driven Visualization

Our ability to generate ever-larger, increasingly-complex data, has established the need for scalable methods that identify, and provide insight into, important variable trends and interactions. Query-driven methods are among the small subset of techniques that are able to address both large and highly complex datasets. This paper presents a new method that increases the utility of query-driven techniques by visually conveying statistical information about the trends that exist between variables in a query. In this method, correlation fields, created between pairs of variables, are used with the cumulative distribution functions of variables expressed in a users query. This integrated use of cumulative distribution functions and correlation fields visually reveals, with respect to the solution space of the query, statistically important interactions between any three variables, and allows for trends between these variables to be readily identified. We demonstrate our method by analyzing interactions between variables in two flame-front simulations.

Query-Driven Visualization of Time-Varying Adaptive Mesh Refinement Data

The visualization and analysis of AMR-based simulations is integral to the process of obtaining new insight in scientific research. We present a new method for performing query-driven visualization and analysis on AMR data, with specific emphasis on time-varying AMR data. Our work introduces a new method that directly addresses the dynamic spatial and temporal properties of AMR grids that challenge many existing visualization techniques. Further, we present the first implementation of query-driven visualization on the GPU that uses a GPU-based indexing structure to both answer queries and efficiently utilize GPU memory. We apply our method to two different science domains to demonstrate its broad applicability.

Synthesis of a statistically exhaustive fluorescent peptide substrate library for profiling protease specificity

A statistically exhaustive, 8800 compound tripeptidal amidomethylcoumarin library was synthesized as discreet compounds using solid-phase combinatorial chemistry. A subset of the compounds was purified by HPLC and tested in a high-throughput fluorometric assay against several known serine and cysteine proteases to demonstrate the utility of this library for profiling protease substrate specificity.

Data Parallel Bin-Based Indexing for Answering Queries on Multi-core Architectures

The multi-core trend in CPUs and general purpose graphics processing units (GPUs) offers new opportunities for the database community. The increase of cores at exponential rates is likely to affect virtually every server and client in the coming decade, and presents database management systems with a huge, compelling disruption that will radically change how processing is done. This paper presents a new parallel indexing data structure for answering queries that takes full advantage of the increasing thread-level parallelism emerging in multi-core architectures. In our approach, our Data Parallel Bin-based Index Strategy (DP-BIS) first bins the base data, and then partitions and stores the values in each bin as a separate, bin-based data cluster. In answering a query, the procedures for examining the bin numbers and the bin-based data clusters offer the maximum possible level of concurrency; each record is evaluated by a single thread and all threads are processed simultaneously in parallel. We implement and demonstrate the effectiveness of DP-BIS on two multi-core architectures: a multi-core CPU and a GPU. The concurrency afforded by DP-BIS allows us to fully utilize the thread-level parallelism provided by each architecture---for example, our GPU-based DP-BIS implementation simultaneously evaluates over 12,000 records with an equivalent number of concurrently executing threads. In comparing DP-BISs performance across these architectures, we show that the GPU-based DP-BIS implementation requires significantly less computation time to answer a query than the CPU-based implementation. We also demonstrate in our analysis that DP-BIS provides better overall performance than the commonly utilized CPU and GPU-based projection index. Finally, due to data encoding, we show that DP-BIS accesses significantly smaller amounts of data than index strategies that operate solely on a columns base data; this smaller data footprint is critical for parallel processors that possess limited memory resources (e.g. GPUs).

Feature identification and extraction in function fields

We present interactive techniques for identifying and extracting features in function fields. Function fields map points in n-dimensional Euclidean space to 1-dimensional scalar functions. Visual feature identification is ac- complished by interactively rendering scalar distance fields, constructed by applying a function-space distance metric over the function field. Combining visual exploration with feature extraction queries, formulated as a set of function-space constraints, facilitates quantitative analysis and annotation. Numerous application domains give rise to function fields. We present results for two-dimensional hyperspectral images, and a simulated time-varying, three-dimensional air quality dataset.

Interactive visualization of function fields by range-space segmentation

We present a dimension reduction and feature extraction method for the visualization and analysis of function field data. Function fields are a class of high‐dimensional, multi‐variate data in which data samples are one‐dimensional scalar functions. Our approach focuses upon the creation of high‐dimensional range‐space segmentations, from which we can generate meaningful visualizations and extract separating surfaces between features. We demonstrate our approach on high‐dimensional spectral imagery, and particulate pollution data from air quality simulations.

HDF5-FastQuery: An API for Simplifying Access to Data Storage,Retrieval, Indexing and Querying

This work focuses on research and development activities that bridge a gap between fundamental data management technology index, query, storage and retrieval and use of such technology in computational and computer science algorithms and applications. The work has resulted in a streamlined applications programming interface (API) that simplifies data storage and retrieval using the HDF5 data I/O library, and eases use of the FastBit compressed bitmap indexing software for data indexing/querying. The API, which we call HDF5-FastQuery, will have broad applications in domain sciences as well as associated data analysis and visualization applications.