IRSJ: incremental refining spatial joins for interactive queries in GIS
An increasing number of emerging web database applications deal with large georeferenced data sets. However, exploring these large data sets through spatial queries can be very time and resource intensive. The need for interactive spatial queries has arisen in many applications such as Geographic Information Systems (GIS) for efficient decision-support. In this paper, we propose a new interactive spatial query processing technique for GIS. We present a family of the Incremental Refining Spatial Join (IRSJ) algorithms that can be used to report incrementally refined running estimates for aggregate queries while simultaneously displaying the actual query result tuples of the data sets sampled so far. Our goal is to minimize the time until an acceptably accurate estimate of the query result is available (to users) measured by a confidence interval. Our approach enables more interactive data exploration and analysis. While similar work has been done in relational databases, to the best of our knowledge, this is the first work using this approach in GIS. We investigate and evaluate different sampling methodologies through extensive experimental performance comparisons. Experiments on both real and synthetic data show an order of magnitude response time improvement relative to the final answer obtained when using a full R-tree join. We also show the impact of different index structures on the performance of our algorithms using three known sampling methods.
spatial join interactive queries gis estimate sampling confidence interval r-tree window query population proportion