With the proliferation of global positioning systems (GPS) enabled devices, a growing number of database systems are capable of storing and querying different spatial objects including points, polylines and polygons. In this paper, we present our experience with supporting one important class of spatial queries in these database systems: distance queries. For example, a traveler may want to find hotels within 500 meters of a nearby beach. In addition, this paper presents new techniques implemented in Oracle Spatial for some distance-related problems, such as the maximum distance between complex spatial objects, and the diameter, the convex hull and the minimum bounding circle of complex spatial objects. We conduct our experiments by utilizing real-world data sets and demonstrate that these distance and distance-related queries can be significantly improved.

Transportation networks including roads and railways, and linear hydrography features like streams and canals are traditionally represented as complex lines in geographic information systems (GIS) and spatial database systems. In addition, as the Global Positioning System (GPS) becomes increasingly ubiquitous, GIS and spatial database systems are also encountering increasing use of trajectories of moving objects, which can also be represented as complex lines with each vertex not only containing location information, but also associated with some additional measures such as time. In this paper, we present our experience with supporting topological relationship queries for these complex lines. Furthermore, this paper presents our experience with supporting topological relationship queries for complex geometry collections, such as a composite hydrography feature, which can be comprised of complex lines (for narrow portions of rivers) and complex polygons (for wide portions of rivers, and lakes). We conduct our experiments by utilizing real-world data sets and demonstrate that topological relationship query performance for both complex lines and complex collections can be significantly improved.

Oracle Spatial and Graph is a geographic information system (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. Accurate selectivity values, especially for queries with multiple predicates requiring joins among numerous tables, are essential for the database optimizer to determine a good execution 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 values from functions implemented within the data cartridge. Although statistics collection for spatial data has been researched in academia for a few years; to the best of our knowledge, this is the first work to present spatial statistics 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 algorithms in presence of complex geometry objects and suggest enhancements which resolve the issues. Secondly, we propose 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 selectivity estimates. Last but not the least, we provide extensive experimental results and an example study which displays the efficacy of our approach on Oracle query performance.

Although geographic information systems (GIS) and spatial database communities have extensively studied topological relationships for more than two decades, there is little literature describing how to efficiently implement them in GIS and spatial database systems. This is rather surprising considering that topological relationship queries are supported in many GIS and spatial database systems including IBM Informix Spatial and Geodetic DataBlades, ESRI SDE, Microsoft SQL server 2008, Oracle Spatial and PostGIS. In order to bridge this gap, we report our experience with implementing several optimization techniques in Oracle Spatial to speed up topological relationship query processing for query windows represented by complex regions (such as polygons or multi-polygons). Our experiments, utilizing real-world data sets, demonstrate that topological relationship query performance can be significantly improved using the proposed techniques.

In this paper we present a more efficient record based caching model than the conventional page (disk block) based scheme. In a record caching model, individual records are stored together in a section of shared memory to form the cache. Traditional relational database systems have individual pages that are stored together in shared memory to form the cache and records are then extracted from these pages on demand. The record cache model has better memory utilization than the page model and also helps reduce overheads like page fetches/writes, page locks and code path.