By Nikos Mamoulis
Spatial database administration offers with the garage, indexing, and querying of information with spatial beneficial properties, akin to situation and geometric volume. Many functions require the effective administration of spatial info, together with Geographic info structures, desktop Aided layout, and placement dependent prone. The aim of this e-book is to supply the reader with an summary of spatial information administration know-how, with an emphasis on indexing and seek options. It first introduces spatial info versions and queries and discusses the most problems with extending a database procedure to help spatial information. It offers indexing methods for spatial information, with a spotlight at the R-tree. question evaluate and optimization suggestions for the most well-liked spatial question varieties (selections, nearest neighbor seek, and spatial joins) are portrayed for facts in Euclidean areas and spatial networks.
The booklet concludes via demonstrating the plentiful program of spatial facts administration expertise on a variety of similar program domain names: administration of spatio-temporal information and high-dimensional characteristic vectors, multi-criteria rating, facts mining and OLAP, privacy-preserving facts publishing, and spatial key-phrase seek.
desk of Contents: advent / Spatial facts / Indexing / Spatial question assessment / Spatial Networks / purposes of Spatial info administration know-how
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Extra resources for Spatial Data Management
Example text
SUMMARY 31 packing M consecutive rectangles (where M is the maximum capacity of an R–tree node), in a leaf node and recursively M consecutive node MBRs at level l to a node at the upper level l + 1. This method constructs an R–tree whose leaf nodes have narrow stripes with large margins as MBRs, which is not expected to perform well for typical spatial queries, as already discussed. A more appropriate sorting key for bulk loading would be the order of the objects in a space filling curve. In this way, objects which are close to each other will be packed in the same leaf node with high probability and the areas (and margins) of the resulting nodes will be small.
INDEXING function split(Node n): Node n1 , Node n2 1. for each dimension i 2. Si := 0; 3. sort the entries using the lower bounds of the rectangles in dimension i; 4. for k:=m to M + 1 − m 5. place first k entries in group A and the remaining ones in group B; 6. Si := Si + margin(A) + margin(B); 7. sort the entries using the upper bounds of the rectangles in dimension i; 8. for k:=m to M + 1 − m 9. place first k entries in group A and the remaining ones in group B; 10. Si := Si + margin(A) + margin(B); 11.
For entries pointing to non-leaf nodes, only the area enlargement and the smallest area are considered. 3. THE R–TREE 29 function choose_subtree(Node n, Entry e): Node leaf 1. if n is a leaf then 2. return n; 3. if entries of n point to leaf nodes then 4. f e := entry in n, which will incur the least overlap with other entries in n if e is accommodated in the node pointed by it. If there are multiple such entries choose the one with the minimum MBR enlargement if e is stored under it. Break ties by choosing entry with the minimum area; 5.