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Rtrees: A Dynamic Index Structure for Spatial Searching
 INTERNATIONAL CONFERENCE ON MANAGEMENT OF DATA
, 1984
"... In order to handle spatial data efficiently, as required in computer aided design and geodata applications, a database system needs an index mechanism that will help it retrieve data items quickly according to their spatial locations However, traditional indexing methods are not well suited to data ..."
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Cited by 2750 (0 self)
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to data objects of nonzero size located m multidimensional spaces In this paper we describe a dynamic index structure called an Rtree which meets this need, and give algorithms for searching and updating it. We present the results of a series of tests which indicate that the structure performs well
Distance Browsing in Spatial Databases
, 1999
"... Two different techniques of browsing through a collection of spatial objects stored in an Rtree spatial data structure on the basis of their distances from an arbitrary spatial query object are compared. The conventional approach is one that makes use of a knearest neighbor algorithm where k is kn ..."
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Cited by 390 (21 self)
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Two different techniques of browsing through a collection of spatial objects stored in an Rtree spatial data structure on the basis of their distances from an arbitrary spatial query object are compared. The conventional approach is one that makes use of a knearest neighbor algorithm where k
The R*tree: an efficient and robust access method for points and rectangles
 INTERNATIONAL CONFERENCE ON MANAGEMENT OF DATA
, 1990
"... The Rtree, one of the most popular access methods for rectangles, is based on the heuristic optimization of the area of the enclosing rectangle in each inner node. By running numerous experiments in a standardized testbed under highly varying data, queries and operations, we were able to design the ..."
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Cited by 1262 (74 self)
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of the following two reasons 1 it efficiently supports point and spatial data at the same time and 2 its implementation cost is only slightly higher than that of other Rtrees.
The Xtree: An index structure for highdimensional data
 In Proceedings of the Int’l Conference on Very Large Data Bases
, 1996
"... In this paper, we propose a new method for indexing large amounts of point and spatial data in highdimensional space. An analysis shows that index structures such as the R*tree are not adequate for indexing highdimensional data sets. The major problem of Rtreebased index structures is the over ..."
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Cited by 592 (17 self)
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In this paper, we propose a new method for indexing large amounts of point and spatial data in highdimensional space. An analysis shows that index structures such as the R*tree are not adequate for indexing highdimensional data sets. The major problem of Rtreebased index structures
Performance of DataParallel Spatial Operations
, 1994
"... The performance of dataparallel algorithms for spatial operations using dataparallel variants of the bucket PMR quadtree, Rtree, and R + tree spatial data structures is compared. The studied operations are data structure build, polygonization, and spatial join in an application domain consisti ..."
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Cited by 23 (6 self)
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The performance of dataparallel algorithms for spatial operations using dataparallel variants of the bucket PMR quadtree, Rtree, and R + tree spatial data structures is compared. The studied operations are data structure build, polygonization, and spatial join in an application domain
Performance of DataParallel Spatial Operations
"... The performance of dataparallel algorithms for spatial operations using dataparallel variants of the bucket PMR quadtree, Rtree, and R +tree spatial data structures is compared. The studied operations are data structure build, polygonization, and spatial join in an application domain consisting ..."
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The performance of dataparallel algorithms for spatial operations using dataparallel variants of the bucket PMR quadtree, Rtree, and R +tree spatial data structures is compared. The studied operations are data structure build, polygonization, and spatial join in an application domain consisting
The R+tree: A dynamic index for multidimensional objects
 PROC. 13TH VLDB CONF
, 1987
"... The problem of indexing multidimensional objects is considered. First, a classification of existing methods is given along with a discussion of the major issues involved in multidimensional data indexing. Second, a variation to Guttman’s Rtrees (R +trees) that avoids overlapping rectangles in inte ..."
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Cited by 345 (19 self)
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The problem of indexing multidimensional objects is considered. First, a classification of existing methods is given along with a discussion of the major issues involved in multidimensional data indexing. Second, a variation to Guttman’s Rtrees (R +trees) that avoids overlapping rectangles
Fast subsequence matching in timeseries databases
 PROCEEDINGS OF THE 1994 ACM SIGMOD INTERNATIONAL CONFERENCE ON MANAGEMENT OF DATA
, 1994
"... We present an efficient indexing method to locate 1dimensional subsequences within a collection of sequences, such that the subsequences match a given (query) pattern within a specified tolerance. The idea is to map each data sequence into a small set of multidimensional rectangles in feature space ..."
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Cited by 533 (24 self)
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space. Then, these rectangles can be readily indexed using traditional spatial access methods, like the R*tree [9]. In more detail, we use a sliding window over the data sequence and extract its features; the result is a trail in feature space. We propose an ecient and eective algorithm to divide
Hilbert Rtree: An Improved Rtree Using Fractals
 Proceedings 20th VLDB Conference
, 1994
"... We propose a new Rtree structure that outperforms all the older ones. The heart of the idea is to facilitate the deferred splitting approach in Rtrees. This is done by proposing an ordering on the Rtree 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 Rtree structure that outperforms all the older ones. The heart of the idea is to facilitate the deferred splitting approach in Rtrees. This is done by proposing an ordering on the Rtree nodes. This ordering has to be 'good', in the sense that it should group &apos
The SRtree: An Index Structure for HighDimensional Nearest Neighbor Queries
, 1997
"... Recently, similarity queries on feature vectors have been widely used to perform contentbased 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 SStree 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 KDBtree. 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
Results 1  10
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