Oracle International Corporation

Bhuvan Bamba

Siva Ravada

Ying Hu

Richard James Anderson, Jr

An optimized method of processing queries requesting a description of a spatial relationship between a test geometry and a query geometry, such as points, lines, polygons, and collections thereof, is disclosed. A first part of the method finds a first spatial relationship between a minimum bounding rectangle (MBR) of the test geometry and an In-Memory R-tree (IMR-tree) built to describe the query geometry. If the first relationship does not specify the requested description, then a second part of the method uses the IMR-tree of the query geometry to find a second spatial relationship between the test geometry itself and the query geometry. Optimizations are applied to the first part and to the second part. Optimizations in the second part depend on the test geometry.

Methods for query processing of topological relationships among complex spatial objects

Techniques are described for memory-efficient spatial histogram construction. A hierarchical spatial index has leaf nodes and non-leaf nodes, each leaf node representing a bounding region containing a spatial object, each non-leaf node representing a bounding region at least partially containing one or more spatial objects. A plurality of selected nodes is selected from the plurality of non-leaf nodes. The plurality of selected nodes includes an ancestor of each leaf node. For each particular node in the plurality of selected nodes, a weight is determined. The weight is based on the number of spatial objects contained within the bounding region of the particular node. A spatial partitioning of the plurality of selected nodes is determined. A spatial histogram is generated based on the spatial partitioning of the weights of the plurality of selected nodes.

Memory-efficient spatial histogram construction

Techniques are described for generating histograms for a multidimensional space. In the presence of large spatial objects, fuzzy splitting techniques are utilized to recursively divide the multidimensional space into partitions, where a single spatial object may belong to multiple partitions. Large spatial objects are essentially broken down into smaller objects that may allow for more efficient partitioning of the multidimensional space. A count of spatial objects in each partition yields a spatial histogram. A spatial object that belongs to multiple partitions may have a weighted count for each of the multiple partitions, based on the extent to which the spatial object overlaps with each partition. Thus, an object that is split among a handful of partitions will only contribute a fraction of a count to each partition. Small partitions having relatively few objects are avoided by refusing to subdivide a partition whose members drop below a threshold number.

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Bhuvan Bamba