Improving the efficiency of subset queries on raster images

Abstract

We propose a parallel method to accelerate the performance of subset queries on raster images. The method, based on map-reduce paradigm, includes two principles from database management systems to improve the performance of subset queries. First, we employ column-oriented storage format for storing locationand weather variables. Second, we improve data locality by storing multidimensional attributes such as space and time in a Hilbert order instead of a serial, row-wise order. We implement the principles in a map-reduce environment, maintaining compatibility with the replication and scheduling constraints. We show through experiments that the techniques improve data locality and increase performance of subset queries, respectively, by 5x and 2x.