Array DBMS: Difference between revisions

Revision as of 18:55, 9 February 2014

Revision as of 07:42, 4 June 2014

Line 4:

Array databases aim at offering flexible, scalable storage and retrieval on this information category.

[[Image:Euclidean neighborhood in n-D arrays.png|framethumb|x110px150px|alt=Euclidean neighborhood of elements in arrays|Euclidean neighborhood of elements in arrays]]

== Overview ==

Line 155:

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 beyoned their main memory.

[[File:Sample heuristic optimization of array query.png|framethumb|x200px400px|alt=Sample rule for heuristic array query optimization|Sample rule for heuristic 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 "averaging over an array resulting from the cell-wise addition of two input images is equivalent to adding the averages 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]] query language introduced before).