M.L. Lo, C.V. Ravishankar, Spatial hash-joins, in: Proceedings ACM SIGMOD Conference, 1996, pp. 247--258.  Home/Search   Document Details and Download   Summary   Related Articles   Check

....not applicable in our domain as there is no notion of fixed arithmetic difference. In the context of spatial and multimedia databases, the problem of computing joins between pairs of spatial entities has been considered, where commonly the predicate of interest is overlap between spatial entities [18, 24, 19] in multiple dimensions. The techniques developed in this paper are related to such join operations. However, the predicates considered as well as the techniques we develop are special to the nature of our structural join problem. In the context of semistructured and XML databases, the issue of ....

M.-L. Lo and C. V. Ravishankar. Spatial hash-joins. Proceedings of SIGMOD, 1996.

....is a distance between two spatial objects # # and # #,and#### is a cutoff distance that is determined by a stopping cardinality # and the spatial attribute values of two data sets # and #. It may then be argued that a spatial distance join query can be processed by a spatial join operation [1, 7, 8, 18, 19, 23] followed by a sort operation. Specifically, if a value can be predicted precisely for a given stopping cardinality #, we can use a spatial join algorithm with a within predicate instead of an intersect predicate to find the # nearest pairs of objects. Then, a sort operation will be performed ....

Ming-Ling Lo and Chinya V. Ravishankar. Spatial hash-join. In Proceedings of the 1996.

....of different algorithms according to the performance requirements and the characteristics of the data set and depending which indexes are preconstructed. Many algorithms for the similarity join are known, partly operating on index structures such as R trees [BKS 93, HJR 97] or based on hashing [LR 96, PD 96] or sorting [SSA 97] For a complete overview of previous and new techniques cf. our tutorial [Bh 01] and a survey which is currently in progress. Motivated by our applications, we have recently published original work in the area of similarity join algorithms. An algorithm based on a new ....

Lo M.-L., Ravishankar C. V.: Spatial Hash Joins, ACM SIGMOD Int. Conf. on Management of Data, 1996.

....s) is a distance between two spatial objects r 2 R and s 2 S, and is a cutoff distance that is determined by a stopping cardinality k and the spatial attribute values of two data sets R and S. It may then be argued that a spatial distance join query can be processed by a spatial join operation [1, 7, 8, 18, 19, 23] followed by a sort operation. Specifically, if a Dmax value can be predicted precisely for a given stopping cardinality k, we can use a spatial join algorithm with a within predicate instead of an intersect predicate to find the k nearest pairs of objects. Then, a sort operation will be performed ....

Ming-Ling Lo and Chinya V. Ravishankar. Spatial hash-join. In Proceedings of the 1996.

....the pages of the R tree. Lo and Ravishankar [31] consider the spatial join problem when the R tree is precomputed on only one of the data sets. They propose to build a seeded tree on the other data set on the fly. The seeded trees can also be used to construct the buckets for spatial hash joins [32]. However, a spatial object may overlap with more than one hash bucket. In this case, either the spatial objects are assigned to multiple buckets or a bucket may join with more than one other bucket. Hjaltason and Samet [22] give an index based incremental algorithm for distance join and distance ....

M.-L. Lo and C. V. Ravishankar. Spatial hash-joins. In SIGMOD, Montreal, Canada, 1996.

....experimental results. We end with a brief discussion in Section 10. 2 Related work Joining two datasets is a costly operation. Current techniques reduce this cost by pruning pairs of data Without Index With Index point data [6, 7, 19, 44] 8, 11, 24] spatial data [3, 12, 29, 30, 36, 38, 45] [5, 20, 23, 31, 32] Table 1. A classification of join techniques. points that do not appear in the final join. They can be classified into two groups based on the data structures they use: 1) no index is built on the datasets, or 2) index is built only on at least one of the datasets. Another way of classifying ....

....the usual data insertion algorithm for R trees. This strategy provides that both the R tree and the seeded tree have similar structure. As a result of this, the number of candidate MBR pairs for join is reduced. The seeded trees can also be used to construct the buckets for spatial hash joins [32]. However, a spatial object may overlap with more than one hash buckets. In this case, either the spatial objects are assigned to multiple buckets or a bucket may join with more than one bucket. Hjaltason and Samet [23] developed an index based incremental algorithm for distance join and distance ....

M.-L. Lo and C. V. Ravishankar. Spatial hash-joins. In SIGMOD, Montreal, Canada, 1996.

....[BKS90] Quad tree [Sam90] or seeded tree [LR94] While some algorithms use preexisting indexes, others build the indexes on the fly. In recent years, some work has focused on non index based spatial join approaches. Two partitionbased spatial join algorithms have been proposed. One of them [LR96] uses an indexed nested loop join to perform the join within each partition. The other [PD96] utilizes a computational geometry based plane10 Table 2: Algorithm Taxonomy Algorithm Name Description Explicit sort ES GRACE sort merge on explicit attributes Hybrid explicit sort ES H Hybrid ....

M.-L. Lo and C. V. Ravishankar. Spatial Hash-Joins. In Proceedings of ACM SIGMOD Conference, pages 247--258, 1996.

....case for storing sets in the nested internal form, since PSJ and even signature nested loops outperform the rewritten queries over the unnested external rep resentation. 1. 1 Related Work Joins have been studied extensively in relational [MK76] Bra84] DKO 84] DNS91] and spatial domains [LR96], PD96] Pointer joins for effi ciently traversing path expressions in object oriented databases has also been studied extensively [DLM93] SC90] However, there is very little previous work on set containment joins. The only reported work of which we are aware is the work by Helmer and ....

M. Lo and C. Ravishankar. Spatial hash-joins. In Proceedings of ACM SIGMOD Conference on Management of Data, Montreal,Quebec, May 1996.

....speed up the queries considerably. Distance based spatial join has been studied extensively. Many existing join algorithms are based on the R trees [2, 3] the Seed trees [9] or the Breadth First approach [6] Other spatial join techniques exist, such as spatial mergejoin [11] spatial hash join [10], size separation spatial join [8] and scalable sweeping based spatial join [1] However, closest pair related join problems have only recently been in the spotlight. For instance, Hjaltason and Samet [5] as well as Corral et al. [4] propose various algorithms to solve the k Closest pair problem. ....

M.-L. Lo and C. V. Ravishankar. Spatial hash-joins. In on Management of Data, pages 247--258, Montreal, Quebec, Canada, 4--6 June 1996.

....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 method, and [APR 98,Vit98] applied the distributes sweeping technique developed in [GTVV93] In [Rot91,SA97] spatial joins with a more general predicate within was considered, which returns objects within some distance. ....

....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 method, and [APR 98,Vit98] applied the distributes sweeping technique developed in [GTVV93] In [Rot91,SA97] spatial joins with a more general predicate within was considered, which returns objects within some distance. Hjaltason and Samet [HS98] developed a distance ....

M-L. Lo and C. V. Ravishankar. Spatial hash-joins. In Proc. ACM SIGMOD Int. Conf. on Management of Data, 1996.

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M.L. Lo, C.V. Ravishankar, Spatial hash-joins, in: Proceedings ACM SIGMOD Conference, 1996, pp. 247--258.

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M.-L. Lo and C. V. Ravishankar. Spatial hash-joins. Proceedings of SIGMOD, 1996.

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M.-L. Lo and C. V. Ravishankar. Spatial hash-joins. Proceedings of SIGMOD, 1996.

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M.-L. Lo and C. Ravishankar. Spatial hash-joins. In Proc. of ACM SIGMOD, pages 247--258, 1996.

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M.-L. Lo and C.V. Ravishankar: "Spatial Hash-Joins", Proceedings ACM SIGMOD Conference, pp.247-258, Montreal, Canada, 1996.

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Lo, M.L., Ravishankar, V.,"Spatial Hash-Joins", ACM-SIGMOD Intl. Conf. On Management of Data, Montreal, Canada, 1996.

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M. Lo and C. V. Ravishankar. Spatial Hash-Joins. In Proc. ACM SIGMOD, pages 247--258, 1996.

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M.-L. Lo and C. V. Ravishankar. Spatial hash-joins. In Proceedings of the 1996.

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Ming-Ling Lo and Chinya V. Ravishankar. Spatial hash-joins. In Proceedings of the ACM SIGMOD International Conference on Management of Data, pages 247--258, June 1996.

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M.-L. Lo and C. V. Ravishankar. Spatial hash-joins. In Proc. of ACM SIGMOD Conf., 1996.

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M. Lo and C.V. Ravishankar. Spatial Hash-Joins. In ACM SIGMOD International Conference on Management of Data, pages 247--258, Montreal, Canada, June 1996.

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M.L. Lo, C.V. Ravishankar, Spatial hash-joins, in: Proceedings ACM SIGMOD Conference, 1996, pp. 247--258.

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M-L. Lo, C.V. Ravishankar. Spatial Hash-Joins. In Proc. ACM SIGMOD Conf., pp 247258, 1996.

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M.-L. Lo, C.V. Ravishankar, Spatial hash-join, in:Proceeding of the 1996.

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M.-L. Lo and C. V. Ravishankar. Spatial hash-joins. Proceedings of SIGMOD, 1996.

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