On the efficient evaluation of array joins

Array Databases close a gap in the database ecosystem by adding modeling, storage, and processing support on multi-dimensional arrays. Declarative queries provide processing of arrays of regularly massive size, such as Tera-to Petabyte datacubes, while allowing internal degrees of freedom in partitioning the large arrays into tractable sub-arrays. Among the important new operations is the array Theta-Join, such as overlaying two images. Evaluation of such joins is complicated by the fact that the participating arrays likely do not align in their partitioning schemes. This can lead to inefficient multiple reads of sub-arrays. We introduce array joins and present an efficient way of pairing corresponding sub-arrays. As a byproduct, this technique delivers information on optimal data placement for parallel join evaluation. The method is implemented in the Array DBMS rasdaman which is in operational use at data centers and mapping agencies.