![]() ![]() These include satellite imagery, climate studies, geosciences, and generally any spatial and spatiotemporal simulations and instrumental measurements, computational fluid dynamics and finite element analysis. ![]() Many scientific and engineering applications involve storage and processing of massive numeric data in the form of multidimensional arrays. We draw some actionable conclusions from the performance numbers. For this purpose, we present a mini-benchmark, featuring a number of typical array access patterns. We evaluate different strategies for partitioning the array content, and for generating the SQL queries that retrieve it on demand. The retrieval is made by automatically generated SQL queries. When a query selects parts of one or more arrays, only the relevant chunks of each array should be retrieved from the relational database. We process queries expressed in an extended graph query language SPARQL, treating arrays as node values and having syntax for specifying array projection, element and range selection operations as part of a query. To minimize data transfer overhead when arrays are large and only parts of arrays are accessed, it is favorable to split these arrays into separately stored chunks. These representations can be stored as binary objects in existing relational database management systems. ![]() Multidimensional numeric arrays are often serialized to binary formats for efficient storage and processing. ![]()
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