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1,274
A New R-tree Space Index Based on the Cluster of Grid Density and Dynamic Grid Division
"... Abstract—The cluster based on the density is a kind of cluster analysis, its main merit is to discover cluster of arbitrary shape and insensitive to the noise data. R-tree organizes the space index according to the spatial data, the spatial overlapping is in a big way, the inquiry efficiency is low, ..."
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Abstract—The cluster based on the density is a kind of cluster analysis, its main merit is to discover cluster of arbitrary shape and insensitive to the noise data. R-tree organizes the space index according to the spatial data, the spatial overlapping is in a big way, the inquiry efficiency is low
Hilbert R-tree: An Improved R-tree Using Fractals
- Proceedings 20th VLDB Conference
, 1994
"... We propose a new R-tree structure that outperforms all the older ones. The heart of the idea is to facilitate the deferred splitting approach in R-trees. This is done by proposing an ordering on the R-tree nodes. This ordering has to be 'good', in the sense that it should group 'simil ..."
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Cited by 223 (11 self)
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We propose a new R-tree structure that outperforms all the older ones. The heart of the idea is to facilitate the deferred splitting approach in R-trees. This is done by proposing an ordering on the R-tree nodes. This ordering has to be 'good', in the sense that it should group &apos
The X-tree: An index structure for high-dimensional data
- In Proceedings of the Int’l Conference on Very Large Data Bases
, 1996
"... In this paper, we propose a new method for index-ing large amounts of point and spatial data in high-dimensional space. An analysis shows that index structures such as the R*-tree are not adequate for indexing high-dimensional data sets. The major problem of R-tree-based index structures is the over ..."
Abstract
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Cited by 592 (17 self)
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In this paper, we propose a new method for index-ing large amounts of point and spatial data in high-dimensional space. An analysis shows that index structures such as the R*-tree are not adequate for indexing high-dimensional data sets. The major problem of R-tree-based index structures
Spatial Joins Using R-trees: Breadth-First Traversal with Global Optimizations
- Proc. of VLDB
, 1997
"... R-tree based spatial join is useful because of both its superior performance and the wide spread implementation of R-trees. We present a new R-tree join method called BFRJ (Breadth-First R-tree Join). BFRJ synchronously traverses both R-trees in breadthfirst order while processing join computation o ..."
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Cited by 96 (4 self)
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R-tree based spatial join is useful because of both its superior performance and the wide spread implementation of R-trees. We present a new R-tree join method called BFRJ (Breadth-First R-tree Join). BFRJ synchronously traverses both R-trees in breadthfirst order while processing join computation
Parallel R-trees
, 1992
"... We consider the problem of exploiting parallelism to accelerate the performance of spatial access methods and specifically, R-trees [11]. Our goal is to design a server for spatial data, so that to maximize the throughput of range queries. This can be achieved by (a) maximizing parallelism for large ..."
Abstract
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Cited by 81 (1 self)
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(`Multiplexed' R-tree). The R-tree code is identical to the one for a single-disk R-tree, with the only addition that we have to decide which disk a newly created R-tree node should be stored in. We propose and examine several criteria to choose a disk for a new node. The most successful one, termed
The SR-tree: An Index Structure for High-Dimensional Nearest Neighbor Queries
, 1997
"... Recently, similarity queries on feature vectors have been widely used to perform content-based retrieval of images. To apply this technique to large databases, it is required to develop multidimensional index structures supporting nearest neighbor queries e ciently. The SS-tree had been proposed for ..."
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Cited by 438 (3 self)
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for this purpose and is known to outperform other index structures such as the R*-tree and the K-D-B-tree. One of its most important features is that it employs bounding spheres rather than bounding rectangles for the shape of regions. However, we demonstrate in this paper that bounding spheres occupy much larger
Parallel R-trees Parallel R-trees
"... Abstract We consider the problem of exploiting parallelism to accelerate the performance of spatial access methods and specifically, R-trees 11]. Our goal is to design a server for spatial data, so that to maximize the throughput of range queries. This can be achieved by (a) maximizing parallelism ..."
Abstract
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(`Multiplexed' R-tree). The R-tree code is identical to the one for a single-disk R-tree, with the only addition that we have to decide which disk a newly created R-tree node should be stored in. We propose and examine several criteria to choose a disk for a new node. The most successful one, termed
Bulk-Insertions into R-Trees
- In Proceedings of ACM International Workshop on Advances in Geographic Information Systems
, 1998
"... A lot of recent work has focussed on bulk loading of data into multidimensional index structures in order to efficiently construct such structures for large datasets. Previous work on bulk loading data focussed at building index structures from scratch, while the problem of bulk insertions into ex ..."
Abstract
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Cited by 3 (1 self)
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into existing index structures has been largely overlooked. In this paper, we address this new problem with particular focus on R-trees -- which are an important class of index structures used widely in commercial database systems. We propose a new technique, which as opposed to the current technique
LINEAR R-TREE REVISITED
"... The problem of finding an optimal splitting of overflowed nodes has a major influence on query performance of the R-tree spatial index structure. Most of the previous split heuristics of R-tree-based index structures have quadratic time and face the problem of increasing overlap of the resulting min ..."
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minimum bounding rectangles (MBRs). In this paper, we propose an efficient heuristic method for handling R-tree node splits. The proposed method is an enhancement of the Linear R-tree method proposed in C. Ang & T. Tan, New linear node splitting algorithm for R-trees, Proc. 5th Int. Symposium
Merging R-trees
"... R-trees, since their introduction in 1984, have been proven to be one of the most well-behaved practical data structures for accommodating dynamic massive sets of geometric objects and conducting a diverse set of queries on such data-sets in real-world applications. In this paper we introduce a new ..."
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R-trees, since their introduction in 1984, have been proven to be one of the most well-behaved practical data structures for accommodating dynamic massive sets of geometric objects and conducting a diverse set of queries on such data-sets in real-world applications. In this paper we introduce a new
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