1Visualization 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.
Summary.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.Scientific data sets are often multisourcecoming from different sources. Although it may be infeasible to physically combine multiple datasets into a single comprehensive dataset, the scientist would often like to treat them as a single logical entity. This paper describes formal conceptual models of multiresolution and distributed multisource scientific data along with an implementation of our multisource model. Our goal is to allow a scientist to describe a dataset that combines several multisource multiresolution datasets into a single conceptual entity and to provide efficient and transparent access to the data based on functionality defined by the model.
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.
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