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Multidimensional array database systems are suited for scientific and engineering applications. Data in these applications is often uncertain and imprecise due to errors in the instruments and observations, etc. There are often correlations exhibited in the distribution of values among the cells of an array. Typically, the correlation is stronger for cells that are close to each other and weaker for cells that are far away. The authors devise a novel data structure, called the A-tree (multidimensional Array tree), demonstrating that by taking advantage of the predictable and structured correlations of multidimensional data, they can have a more efficient way of modeling and answering queries on large-scale array data.
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