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Dive into the research topics where Ted M. Sparr is active.

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Featured researches published by Ted M. Sparr.


ieee visualization | 1993

An Extended Schema Model for Scientific Data

David T. Kao; R. Daniel Bergeron; Ted M. Sparr

The absence of a uniform and comprehensive representation for complex scientific data makes the adaptation of database technology to multidisciplinary research projects difficult. In this paper, we clarify the taxonomy of data representations required for scientific database systems. Then, based on our proposed scientific database environment, we present a scientific data abstraction at the conceptual level, a schema model for scientific data. This schema model allows us to store and manipulate scientific data in a uniform way independent of the implementation data model. We believe that more information has to be maintained as metadata for scientific data analysis than in statistical and commercial databases. Clearly, metadata constitutes an important part of our schema model. As part of the schema model, we provide an operational definition for metadata. This definition enables us to focus on the complex relationship between data and metadata.


Focus on Scientific Visualization | 1991

A Visualization-Based Model for a Scientific Database System

Ted M. Sparr; R. Daniel Bergeron; Loren D. Meeker

The goal of this multi-disciplinary project is to design and prototype a new approach in database environments aimed at supporting collaborative scientific research. The design integrates scientific data visualization and mathematical and statistical analysis tools with database support in a highly interactive environment. A new schema model for scientific data is proposed. The new scientific database model is founded on the notion that a query of a scientific database conceptually creates new derived data whose relationship to the parent database is defined by the query. The System uses a process flow graph as the mechanism for representing queries. Each query, in principle, leads to the discovery of some form of structure in the data which is explicitly represented by the results of the query, or which is hypothesized by the scientist as a result of the current and previous queries.


Archive | 2003

A Data Model for Distributed Multiresolution Multisource Scientific Data

Philip J. Rhodes; R. Daniel Bergeron; Ted M. Sparr

Modern dataset sizes present major obstacles to understanding and interpreting the significant underlying phenomena represented in the data. There is a critical need to support scientists in the process of interactive exploration of these very large data sets. Using multiple resolutions of the data set (multiresolution), the scientist can identify potentially interesting regions with a coarse overview, followed by narrower views at higher resolutions.


visualization and data analysis | 2005

Out of core visualization using iterator aware multidimensional prefetching

Philip J. Rhodes; Xuan Tang; R. Daniel Bergeron; Ted M. Sparr

Visualization of multidimensional data presents special challenges for the design of efficient out-of-core data access. Elements that are nearby in the visualization may not be nearby in the underlying data file, which can severely tax the operating system’s disk cache. The Granite Scientific Database System can address these problems because it is aware of the organization of the data on disk, and it knows the visualization method’s pattern of access. The access pattern is expressed using a toolkit of iterators that both describe the access pattern and perform the iteration itself. Because our system has knowledge of both the data organization and the access pattern, we are able to provide significant performance improvements while hiding the details of out-of-core access from the visualization programmer. This paper presents a brief description of our disk access system placing special emphasis on the benefits offered to a visualization application. We describe a simple demonstration application that shows dramatic performance improvements when used with the 39GB Visible Woman Dataset.


conference on current trends in theory and practice of informatics | 2002

Database Support for Multisource Multiresolution Scientific Data

Philip J. Rhodes; R. Daniel Bergeron; Ted M. Sparr

We extend database technology to provide more meaningful support for exploration of scientific data. We have developed a new data model that incorporates spatial semantics with localized error and are implementing a prototype database system based on the model. Our data model and system focus on support for retrieval and visualization of gridded scientific data at multiple resolutions. While these semantics may not apply naturally to every scientific application, they are common to many. This paper summarizes the data model and describes the key functionality of our prototype system.


statistical and scientific database management | 1998

Efficient proximity search in multivariate data

D.T. Kao; R.D. Bergeron; Ted M. Sparr

Proximity search is an important type of database query which is essential to many practical applications involving various types of metric data, including multivariate data with distance function. Point spatial data is a popular subset of metric data in which each data record corresponds to a point in a multidimensional space, and the proximity is represented as a distance function, such as the Euclidean distance, defined on the multidimensional space. Numerous hierarchical data structures, under the name of point spatial data structures, have been developed for implementing efficient spatial proximity searches. Much less work has been done on developing general hierarchical metric data structures for general metric data, such as non-spatial multivariate data. This paper presents an innovative approach for deriving a new class of hierarchical metric data structures from existing point spatial data structures. Instead of performing direct decomposition on metric data as is done for previous hierarchical data structures such as metric trees and vp-trees, we define a class of simple proximity-preserving mappings from metric data to multidimensional spaces, which we call multipolar mappings. By applying multipolar mappings to metric data, hierarchical decompositions can be done in multidimensional space, and various point spatial data structures, such as quadtree, octree, or k-d tree, can be utilized for storing and accessing metric data based on proximity.


visualization and data analysis | 2007

Spatial prefetching for out-of-core visualization of multidimensional data

Dan R. Lipsa; Philip J. Rhodes; R. Daniel Bergeron; Ted M. Sparr

In this paper we propose a technique called storage-aware spatial prefetching that can provide significant performance improvements for out-of-core visualization. This approach is motivated by file chunking in which a multidimensional data file is reorganized into multidimensional sub-blocks that are stored linearly in the file. This increases the likelihood that data close in the n-dimensional volume represented by the file will be closer together in the physical file. Chunking has been demonstrated to improve the typical access to such data, but it requires a complete re-organization of the file and sometimes efficient access is only achieved if multiple different chunking organizations are maintained simultaneously. Our approach can be thought of as on-the-fly chunking, but it does not require physical re-organization of the data or multiple copies with different formats. We also describe an implementation of our technique and provide some performance results that are very promising.


international symposium on visual computing | 2009

Dynamic Chunking for Out-of-Core Volume Visualization Applications

Dan R. Lipsa; R. Daniel Bergeron; Ted M. Sparr; Robert S. Laramee

Given the size of todays data, out-of-core visualization techniques are increasingly important in many domains of scientific research. In earlier work a technique called dynamic chunking [1] was proposed that can provide significant performance improvements for an out-of-core, arbitrary direction slicer application. In this work we validate dynamic chunking for several common data access patterns used in volume visualization applications. We propose optimizations that take advantage of extra knowledge about how data is accessed or knowledge about the behavior of previous iterations and can significantly improve performance. We present experimental results that show that dynamic chunking has performance close to regular chunking but has the added advantage that no reorganization of data is required. Dynamic chunking with the proposed optimizations can be significantly faster on average than chunking for certain common data access patterns.


Data Visualization: The State of the Art | 2003

A Data Model for Adaptive Multi-Resolution Scientific Data

Philip J. Rhodes; R. Daniel Bergeron; Ted M. Sparr

Representing data using multiresolution is a valuable tool for the interactive exploration of very large datasets. Current multiresolution tools are written specifically for a single kind of multiresolution data. As a step toward developing general purpose multiresolution tools, we present here a model that represents a wide range of multiresolution data within a single paradigm. In addition, our model provides support for working with multiresolution data in a distributed environment.


eurographics | 2003

Uncertainty Visualization Methods in Isosurface Rendering

Philip J. Rhodes; Robert S. Laramee; R. Daniel Bergeron; Ted M. Sparr

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R. Daniel Bergeron

University of New Hampshire

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David T. Kao

University of New Hampshire

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Andrew Foulks

University of New Hampshire

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R.D. Bergeron

University of New Hampshire

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