Oracle America Inc.

Oracle Spatial and Graph is a geographic information sys-tem (GIS) which provides users the ability to store spatial data alongside conventional data in Oracle. As a result of the coexistence of spatial and other data, we observe a trend towards users performing increasingly complex queries which involve spatial as well as non-spatial predicates. Ac-curate selectivity values, especially for queries with multi-ple predicates requiring joins among numerous tables, are essential for the database optimizer to determine a good ex-ecution plan. For queries involving spatial predicates, this requires that reasonably accurate statistics collection has been performed on the spatial data. For extensible data cartridges such as Oracle Spatial and Graph, the optimizer expects to receive accurate predicate selectivity and cost val-ues from functions implemented within the data cartridge. Although statistics collection for spatial data has been re-searched in academia for a few years; to the best of our knowledge, this is the first work to present spatial statis-tics collection implementation details for a commercial GIS database. In this paper, we describe our experiences with implementation of statistics collection methods for complex geometry objects within Oracle Spatial and Graph. Firstly, we exemplify issues with previous partitioning-based algo-rithms in presence of complex geometry objects and suggest enhancements which resolve the issues. Secondly, we pro-pose a main memory implementation which not only speeds up the disk-based partitioning algorithms but also utilizes existing R-tree indexes to provide surprisingly accurate se-lectivity estimates. Last but not the least, we provide ex-tensive experimental results and an example study which displays the efficacy of our approach on Oracle query per-formance. 1