T. Brinkho#, H.P. Kriegel, B. Seeger, E#cient processing of spatial joins using R-trees, in: Proceedings ACM SIGMOD Conference, 1993, pp. 237--246. Home/Search Document Not in Database Summary Related Articles Check |
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Local Polyhedra and Geometric Graphs - Erickson (2003) (Correct)
....apply immediately to any other type of object represented by a simplicial piecewise linear complex. For example, given two local planar straightline graphs, we can overlay them in O(n log n) time either by using the standard sweep line algorithm, or by merging a graded (semi )R tree for each graph [5, 27]. Similarly, given two local finite element meshes, we can find all pairs of overlapping elements in near linear time using recursive bisection. In particular, this technique is e#cient for the well shaped meshes produced by Delaunay refinement algorithms. These and other algorithmic applications ....
T. Brinkho#, H.-P. Kriegel, and B. Seeger. E#cient processing of spatial joins using R-trees. Proc. ACM SIGMOD Conf. on Management of Data, 237--246, 1993.
Efficient Record Linkage in Large Data Sets - Jin, Li, Mehrotra (2003) (2 citations) (Correct)
....of those similar string pairs. In Section 6.2 we will give experimental results on choosing # # . 4.2 Finding Object Pairs within # # We want to find all those object pairs whose new distance is within this new threshold # # . Similarity joins over multidimensional spaces have been studied in [1, 6, 9, 21, 22, 24, 28, 33, 32]. Many algorithms can be used in this step. In this paper we use a simplified version of the algorithm in [16] as an example. We could instead have chosen any of those algorithms for our purpose. We chose the algorithm in [16] due to its simplicity and availability of the code. This approach ....
T. Brinkho#, H.-P. Kriegel, and B. Seeger. E#cient processing of spatial joins using r-trees. In SIGMOD, pages 237--246, 1993.
On Multi-Way Spatial Joins with Direction Predicates - Zhu, Su, Ibarra (Correct)
....case. A lot of work has been done on 2 way spatial joins. Most previous work focused on joins with the predicate region intersection. The intersection spatial join algorithms can be roughly divided into two categories: ones that use spatial index structures and ones that do not. Algorithms of [BKS93,HJR97] use spatial index structures such as R trees, R trees, R # trees. Interval B trees [ZSI99] and external segment trees [Arg95] can also be used. Algorithms without using index structures were reported in [PD96] LR96] and [APR 98,Vit98] PD96] and [LR96] used a partition based ....
T. Brinkho#, H-P. Kriegel, and B. Seeger. E#cient processing of spatial join using R-trees. In Proc. ACM SIGMOD Int. Conf. on Management of Data, 1993.
Processing a Multimedia Join through the Method of Nearest.. - Kosch, Atnafu (2001) (Correct)
....queries involve two or more (see e.g. 9] sets of spatial data and combine pairs (or tuples) of spatial objects that satisfy a given predicate. For instance, one might be interested in finding all the pairs of objects that intersect with each other (intersection join) Brinkho# et al. present in [10] a very detailed analysis of di#erent implementation strategies for an intersection join. They show that a spatial sort merge based join based on a plane sweep technique outperforms a nested loop one. However, the plane sweep technique proposed for an intersection join cannot apply for the purpose ....
T. Brinkho#, H.-P.Kriegel, and B. Seeger. E#cient processing of spatial joins using R-trees. In Proceedings of the ACM SIGMOD International Conference on Management of Data, pages 10--15, Washington, USA, June 1993. 15
Extending Rectangle Join Algorithms for Rectilinear Polygons - Zhu, Su, Ibarra (2000) (Correct)
....performs a spatial join on approximations of the objects; and (2) a refinement step that checks whether the objects discovered by the filter step actually intersect. A great deal of research has been done to speed up the filter step and to reduce the size of the result it produces. Many algorithms [16, 18, 17, 20, 3, 7, 1, 19, 22, 15, 11, 2, 25] focus on the filter step and use the minimum bounding rectangle (MBR) the smallest rectangle containing a spatial object, as an approximation of the object. In this paper, we call these algorithms rectangle join algorithms. Rectangle join evaluation has been the focus of the study on spatial ....
....addition, 2] uses a distributed sweeping technique [9] to achieve IO e#ciency. The algorithms in the second category requires additional index structures. The algorithm in [20] uses join indices [24] that are actually partially precomputed join result. R # trees [4] are used in the algorithms of [7, 11]. 1] extended segment trees [5] for external memory to improve IO performance. 22] used a combination of a space filling curve and filter trees that extends the idea of quad trees (see [21] In [25] we developed interval B trees that combine segment trees and B trees and an algorithm ....
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T. Brinkho#, H-P. Kriegel, and B. Seeger. E#cient processing of spatial join using R-trees. In Proc. Int. Conf. on Data Engineering, 1993.
Algorithms for Processing K-closest-pair Queries.. - Corral.. (2004) (Correct)
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T. Brinkho#, H.P. Kriegel, B. Seeger, E#cient processing of spatial joins using R-trees, in: Proceedings ACM SIGMOD Conference, 1993, pp. 237--246.
SINA: Scalable Incremental Processing of Continuous Queries .. - Mokbel, Xiong, Aref (2004) (1 citation) (Correct)
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Thomas Brinkho#, Hans-Peter Kriegel, and Bernhard Seeger. E#cient Processing of Spatial Joins Using R-Trees. In SIGMOD, 1993.
Scalable Spatio-temporal Continuous Query.. - Xiong, Mokbel.. (Correct)
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T. Brinkho#, H.-P. Kriegel, and B. Seeger. E#cient Processing of Spatial Joins Using R-Trees. In P. Buneman and S. Jajodia, editors, SIGMOD, 1993.
Efficient Querying of Constantly Evolving Data - Kalashnikov (2003) (Correct)
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T. Brinkho#, H.-P. Kriegel, and B. Seeger. E#cient processing of spatial joins using R-trees. In Proceedings of the ACM SIGMOD International Conference on Management of Data, 1993.
Scalable Spatio-temporal Continuous Query.. - Xiong, Mokbel.. (Correct)
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T. Brinkho#, H.-P. Kriegel, and B. Seeger. E#cient Processing of Spatial Joins Using R-Trees. In P. Buneman and S. Jajodia, editors, SIGMOD, 1993.
Spatial Cone Tree: An Index Structure for Correlation-based - Similarity Queries On (Correct)
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T. Brinkho#, H. Kriegel, and B. Seeger. E#cient Processing of Spatial Join Using R-trees. In ACM SIGMOD, 1993.
An Experimental Performance Evaluation of.. - Jeong, Paton.. (2004) (Correct)
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T. Brinkho#, H. Kriegel, and B. Seeger. E#cient Processing of Spatial Joins Using R-trees. In ACM SIGMOD International Conference on Management of Data, pages 237--246, Washington, D.C., May 1993.
SINA: Scalable Incremental Processing of Continuous Queries .. - Mokbel, Xiong, Aref (2004) (1 citation) (Correct)
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T. Brinkho#, H.-P. Kriegel, and B. Seeger. E#cient Processing of Spatial Joins Using R-Trees. In P. Buneman and S. Jajodia, editors, SIGMOD, 1993.
Algorithms for processing K-closest-pair queries.. - Corral.. (2004) (Correct)
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T. Brinkho#, H.P. Kriegel, B. Seeger, E#cient processing of spatial joins using R-trees, in: Proceedings ACM SIGMOD Conference, 1993, pp. 237--246.
NNH: Improving Performance of Nearest-Neighbor Searches Using .. - Jin, Koudas, Li (2004) (1 citation) (Correct)
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Brinkho#, T., Kriegel, H.P., Seeger, B.: E#cient Processing of Spatial Joins using R-trees. Proceedings of ACM SIGMOD (1993) 237--246
NNH: Improving Performance of Nearest-Neighbor Searches Using .. - Jin, Koudas, Li (2003) (1 citation) (Correct)
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Brinkho#, T., Kriegel, H.P., Seeger, B.: E#cient Processing of Spatial Joins using R-trees. Proceedings of ACM SIGMOD (1993) 237--246
Local Polyhedra and Geometric Graphs - Erickson (2003) (Correct)
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T. Brinkho#, H.-P. Kriegel, and B. Seeger. E#cient processing of spatial joins using R-trees. Proc. ACM SIGMOD Conf. on Management of Data, 237--246, 1993.
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