ABSTRACT Optimization of Multiple Continuous Queries over Streaming Satellite Data

Remotely sensed data, in particular satellite imagery, play many important roles in environmental applications and models. 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 entire system. Based on the widely used Geographic Resources Analysis Support System (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 pipeline designed to share intermediate results. For each new image from the stream, the optimizer generates an execution plan that is 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 Geostationary Operational Environmental Satellite (GOES). Continuous queries are defined in a way similar to the OGC Web Map Services (WMS) query specification. Using predicted query patterns over the visible hemisphere of GOES experimental results indicate that multi-query optimized plans can improve performance significantly when compared to queries that are executed separately