Spatial View Materialization Technique by using R - Tree Reconstruction

Abstract

In spatial database system, spatial view is supported for efficient access method to spatial database and is managed by materialization and non-materialization technique. In non-materialization technique, repeated execution on the same query makes problems such as the bottle-neck effect of server-side and overloads on a network. In materialization technique, view maintenance technique is very difficult and maintenance cost is too high when the base table has been changed. In this paper, the SVMT (Spatial View Materialization Technique) is proposed by using R-tree re-construction. The SVMT is a technique which constructs a spatial index according to the distribution ratio of objects in spatial view. This ratio is computed by using a SVHR (Spatial View Height in R-tree) and SVOC (Spatial View Object Count). If the ratio is higher than the average, a spatial view is materialized and the R-tree index is re-used. In this case, the root node of this index is exchanged a node which has a MBR (Minimum Boundary Rectangle) value that can contains the whole region of spatial view at a minimum size. Otherwise, a spatial view is materialized and the R-tree is re-constructed. In this technique, the information of spatial view is managed by using a SVIT (Spatial View Information Table) and is stored on the record of this table. The proposed technique increases the speed of response time through fast query processing on a materialized view and eliminates additional costs occurred from repeatable query modification on the same query. With these advantages, it can greatly minimize the network overloads and the bottle-neck effect on the server.

Cite this paper

@article{jeong2001spatial,
  title = {{Spatial View Materialization Technique by using R - Tree Reconstruction}},
  author = {Jeong, Bo-Heung and Bae, Hae-Yeong},
  journal = {KIPS Transactions on Computer and Communication Systems},
  volume = {8},
  number = {4},
  pages = {377--386},
  year = {2001}
}

Made from the index record. Check names, capitalisation and page numbers against the paper. Need another style? AI for Research has over 10,000.

Related papers