Array DBMS: Difference between revisions

Revision as of 02:26, 5 January 2020

Revision as of 12:09, 5 January 2020

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A tile-based storage structure suggests a tile-by-tile processing strategy (in [[rasdaman]] called ''tile streaming''). A large class of practically relevant queries can be evaluated by loading tile after tile, thereby allowing servers to process arrays orders of magnitude beyond their main memory.

[[File:Sample heuristicrule optimizationfor ofheuristic array query optimization.png|thumb|400px|alt=SampleTransformation ruleof fora heuristicquery arrayto querya optimization|Samplemore ruleefficient, forbut heuristicequivalent version during array query optimization]]

Due to the massive sizes of arrays in scientific/technical applications in combination with often complex queries, optimization plays a central role in making array queries efficient. Both hardware and software parallelization can be applied. An example for heuristic optimization is the rule "averagingmaximum overvalue of an array resulting from the cell-wise addition of two input images is equivalent to adding the averagesmaximum values of each input array". By replacing the left-hand variant by the right-hand expression, costs shrink from three (costly) array traversals to two array traversals plus one (cheap) scalar operation (see Figure, which uses the [[rasdaman]]SQL/MDA query language introduced beforestandard).

== Application domains ==