Applications of spatial data structures: Computer graphics, image processing, and GIS
Applications of spatial data structures: Computer graphics, image processing, and GIS
The design and analysis of spatial data structures
The design and analysis of spatial data structures
Discrete cosine transform: algorithms, advantages, applications
Discrete cosine transform: algorithms, advantages, applications
Hierarchical address vector quantization for image coding
CVGIP: Graphical Models and Image Processing
Spatial data models and query processing
Modern database systems
Multidimensional binary search trees used for associative searching
Communications of the ACM
A class of data structures for associative searching
PODS '84 Proceedings of the 3rd ACM SIGACT-SIGMOD symposium on Principles of database systems
SSD '93 Proceedings of the Third International Symposium on Advances in Spatial Databases
Abstracts: A Latency-Hiding Technique for High-Capacity
Abstracts: A Latency-Hiding Technique for High-Capacity
Multidimensional data structures for spatial applications
Algorithms and theory of computation handbook
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The datasets generated by satellite observations and supercomputer simulations are overwhelming conventional methods of storage and access, leading to unreasonably long delays in data analysis. The major problem that we address is the slow access, from large datasets in archival storage, to small subsets needed for scientific visualization and analysis. The goal is to minimize the amount of storage that has to be read when a subset of the data is needed. A second goal is to enhance the accessibility of data subsets by applying data reduction and indexing methods to the subsets. The reduced format allows larger datasets to be stored on local disk for analysis. Data indexing permits efficient manipulation of the data, and thus improves the productivity of the researcher. A data structure called the ATree is described that meets the demands of interactive scientific applications. The ATree data structure is suitable for storing data abstracts as well as original data. It allows quick access to a subset of interest and is suitable for feature-based queries. It intrinsically partitions the data and organizes the chunks in a linear sequence on secondary/tertiary storage. It can store data at various resolutions and incorporates hierarchical compression methods.