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Efficient Processing of Spatial Joins Using RTrees
, 1993
"... Abstract: In this paper, we show that spatial joins are very suitable to be processed on a parallel hardware platform. The parallel system is equipped with a socalled shared virtual memory which is wellsuited for the design and implementation of parallel spatial join algorithms. We start with an a ..."
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Cited by 321 (13 self)
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Abstract: In this paper, we show that spatial joins are very suitable to be processed on a parallel hardware platform. The parallel system is equipped with a socalled shared virtual memory which is wellsuited for the design and implementation of parallel spatial join algorithms. We start with an algorithm that consists of three phases: task creation, task assignment and parallel task execution. In order to reduce CPU and I/Ocost, the three phases are processed in a fashion that preserves spatial locality. Dynamic load balancing is achieved by splitting tasks into smaller ones and reassigning some of the smaller tasks to idle processors. In an experimental performance comparison, we identify the advantages and disadvantages of several variants of our algorithm. The most efficient one shows an almost optimal speedup under the assumption that the number of disks is sufficiently large. Topics: spatial database systems, parallel database systems 1
Querying by Spatial Structure
, 1998
"... : Structural queries constitute a special form of contentbased retrieval where the user specifies a set of spatial constraints among query variables and searches for all configurations of actual objects that (totally or partially) match these constraints. Processing of such queries can be though ..."
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Cited by 28 (9 self)
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: Structural queries constitute a special form of contentbased retrieval where the user specifies a set of spatial constraints among query variables and searches for all configurations of actual objects that (totally or partially) match these constraints. Processing of such queries can be thought of as a general form of spatial joins, i.e., instead of pairs, the result consists of ntuples of objects, where n is the number of query variables. In this paper we propose a flexible framework which permits the representation of configurations in different resolution levels and supports the automatic derivation of similarity measures. We subsequently describe three algorithms for structural query processing which integrate constraint satisfaction with spatial indexing. For each algorithm we apply several optimization techniques and experimentally evaluate performance using real data. Abstract Tracking Number: 628 Correspondence should be addressed to Dimitris Papadias. Tel: ++85...
Integration of Spatial Join Algorithms for Joining Multiple Inputs
, 1998
"... Several techniques that compute the join between two spatial datasets have been proposed during the last decade. Among these methods, some consider existing indices for the joined inputs, while others treat datasets with no index, thus providing solutions for the case where at least one input comes ..."
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Cited by 8 (1 self)
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Several techniques that compute the join between two spatial datasets have been proposed during the last decade. Among these methods, some consider existing indices for the joined inputs, while others treat datasets with no index, thus providing solutions for the case where at least one input comes as an intermediate result of another database operator. In this paper we analyze previous work on spatial joins and propose a novel algorithm, called slot index spatial join (SISJ), that efficiently computes the spatial join between two inputs, only one of which is indexed by an Rtree. Going one step further, we show how SISJ and other spatial join algorithms can be implemented as operators in a database environment that joins more than two spatial inputs. We study the differences between relational and spatial multiway joins, and propose a dynamic programming algorithm that optimizes the execution of complex spatial queries. Contact Author: Dimitris Papadias Tel: ++85223586971 http://www...
Constraintbased Algorithms for Computing Clique Intersection Joins
 Proc. ACMGIS
, 1998
"... Spatial joins constitute one of the most active research topics in spatial query processing. This paper deals with the processing of clique intersection joins using Rtrees. A clique intersection join will retrieve all ntuples of objects that pairwise overlap. The corresponding MBRbased filter st ..."
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Cited by 4 (1 self)
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Spatial joins constitute one of the most active research topics in spatial query processing. This paper deals with the processing of clique intersection joins using Rtrees. A clique intersection join will retrieve all ntuples of objects that pairwise overlap. The corresponding MBRbased filter step retrieves ntuples of rectangles that intersect at some common point. Here we modify three algorithms, first proposed in [13], for the specific problem and experimentally evaluate their performance using data sets of various densities. 1.1 Keywords Spatial joins, spatial query processing, multiway joins
Spatial Join Selectivity Using Power Laws
, 2000
"... We discovered a surprising law governing the spatial join selectivity across two sets of points. An example of such a spatial join is "find the libraries that are within 10 miles of schools". Our law dictates that the number of such qualifying pairs follows a power law, whose exponent we call "pai ..."
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We discovered a surprising law governing the spatial join selectivity across two sets of points. An example of such a spatial join is "find the libraries that are within 10 miles of schools". Our law dictates that the number of such qualifying pairs follows a power law, whose exponent we call "paircount exponent" (PC). We show that this law also holds for selfspatialjoins ("find schools within 5 miles of other schools") in addition to the general case that the two pointsets are distinct. Our law holds for many real datasets, including diverse environments (geographic datasets, feature vectors from biology data, galaxy data from astronomy). In addition, we introduce the concept of the BoxOccupancyProductSum (BOPS) plot, and we show that it can compute the paircount exponent in a timely manner, reducing the run time by orders of magnitude, from quadratic to linear. Due to the paircount exponent and our analysis (Law 1), we can achieve accurate selectivity estimates in c...