diff --git a/doc/html/geometry_index/r_tree/introduction.html b/doc/html/geometry_index/r_tree/introduction.html index 41dd3a17f..c326a0c36 100644 --- a/doc/html/geometry_index/r_tree/introduction.html +++ b/doc/html/geometry_index/r_tree/introduction.html @@ -31,7 +31,7 @@ by Antonin Guttman in 1984 [1] as an expansion of B-tree for multi-dimensional data. It may be used to store points or volumetric data in order to perform a spatial query later. This query may return objects that are inside some area or are - close to some point in space. + close to some point in space [2].
The R-tree structure is presented on the image below. Each R-tree's node @@ -51,7 +51,7 @@
The R-tree is a self-balanced data structure. The key part of balancing algorithm - is node splitting algorithm [2] [3]. Each algorithm produces different splits so the internal structure + is node splitting algorithm [3] [4]. Each algorithm produces different splits so the internal structure of a tree may be different for each one of them. In general more complex algorithms analyses elements better and produces less overlapping nodes. In the searching process less nodes must be traversed in order to find desired @@ -192,8 +192,9 @@ capable to store arbitrary Value type,
[2] + Cheung, K.; Fu, A. (1998). Enhanced Nearest Neighbour Search + on the R-tree +
[3] Greene, D. (1989). An implementation and performance analysis of spatial data access methods
[3] +
[4] Beckmann, N.; Kriegel, H. P.; Schneider, R.; Seeger, B. (1990). The R*-tree: an efficient and robust access method for points and rectangles
Last revised: November 26, 2012 at 22:11:47 GMT |
+Last revised: November 27, 2012 at 12:09:04 GMT |
O;
+
+ static boost::shared_ptr O;
+
+ static boost::shared_ptr