Map algebra on raster datasets represented by compact data structures

Abstract

The increase in the size of data repositories has forced the design of new computing paradigms to be able to process large volumes of data in a reasonable amount of time. One of them is in‐memory computing, which advocates storing all the data in main memory to avoid the disk I/O bottleneck. Compression is one of the key technologies for this approach. For raster data, a compact data structure, called k2$$ {k}^2 $$‐raster, have been recently been proposed. It compresses raster maps while still supporting fast retrieval of a given datum or a portion of the data directly from the compressed data. k2$$ {k}^2 $$‐raster's original work introduced several queries in which it was superior to competitors. However, to be used as the basis of an in‐memory system for raster data, it is mandatory to demonstrate its efficiency when performing more complex operations such as the map algebra operators. In this work, we present the algorithms to run a set of these operators directly on k2$$ {k}^2 $$‐raster without a decompression procedure.