Optimization of multiple continuous queries over streaming satellite data

Abstract

Remotely sensed data, in particular satellite imagery, play many important roles in environmental applications. In particular applications that study rapid changes in the environment require frequent access to these data. For continuous data products, users are often interested in formulating continuous queries that deliver results for each incoming image. In the presence of multiple continuous queries, there is clearly an opportunity to share common intermediate data and thus, increase the overall processing speed of the system.Based on the widely used GRASS, this paper describes a system that realizes multiple query processing using two major components. A query optimizer maintains the current set of active continuous queries. Queries are organized into a single processing plan designed to share intermediate results. For each new image from the stream, the optimizer generates an execution plan specific to the active queries. The query executor then rewrites this plan into a set of geospatial processing steps and executes the plan.We detail experiments using data from NOAA's GOES. Continuous queries are defined in a way similar to the OGC WMS query specification. Using predicted query patterns over the visible hemisphere of GOES, experimental results indicate that multiple-query optimized plans can improve performance significantly when compared to queries that are executed separately.