The R*-tree: an efficient and robust access method for points and rectangles
SIGMOD '90 Proceedings of the 1990 ACM SIGMOD international conference on Management of data
Multidimensional access methods
ACM Computing Surveys (CSUR)
Indexing medium-dimensionality data in Oracle
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
External memory algorithms and data structures: dealing with massive data
ACM Computing Surveys (CSUR)
Quadtree and R-tree indexes in oracle spatial: a comparison using GIS data
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
R-trees: a dynamic index structure for spatial searching
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
Indexing non-uniform spatial data
IDEAS '97 Proceedings of the 1997 International Symposium on Database Engineering & Applications
Improving performance with bulk-inserts in Oracle R-trees
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Improving the R*-tree with outlier handling techniques
Proceedings of the 13th annual ACM international workshop on Geographic information systems
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The R*-tree is a state-of-the-art spatial index structure. It has already found its way into commercial systems. The most important improvement of the R*-tree over the original R-tree is that it utilizes forced reinsertion. That is, if a disk page overflows, some objects are removed from the page and reinserted into the index. The goals are: (a) to reduce the MBR area; and (b) to keep the shape of the MBR close to a square. However, no existing work consists of a unified metric which can be used to balance the two criteria. For example, if there are two methods to remove objects from a rectangle, and one results in a rectangle with smaller area, while the other results in a square with slightly larger area, which method shall we choose? The R*-tree algorithm selects objects whose distances to the center of the page's MBR are the largest. However, this is not optimal. In this paper, we formally define the quality of a rectangle and the gain to shrink a rectangle. Then we provide algorithms to shrink the MBRs with the goal to maximize the gain. The algorithms are experimentally compared with the R*-tree's reinsertion algorithm. Furthermore, as the opposite of gain, we define the loss of expanding a rectangle. While inserting an object into the R*-tree, we need to choose a sub-tree to put the object in. With the new metric, we can choose the sub-tree with the least loss. Finally, we integrate the new algorithms into the R*-tree.