J. Orenstein: "Spatial query processing in an object-oriented database system", Proceedings of the 1986 ACM SIGMOD Conference, pp.326-336, Washington DC, 1986.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....If R is organized in chunks, COM PUTEWAVELET can perform the decomposition in a single pass over the tuples of R. Note that such data organizations have already been proposed in earlier work (e.g. the chunked file organization of Deshpande et al. 37] and Orenstein s z order linearization [73, 109]) where they have been shown to have significant 123 3 1 3 1 4 2 2 6 2 3 6 6 6 5 3 7 1 4 1 1 1 7 0 4 7 6 3 6 6 5 2 3 6 0 1 0 7 3 1 7 1 4 4 Chunk2 Chunk1 Chunk3 Chunk4 (a) b) c) d) D1 D2 Figure 7.3: a) An example relation R with 2 attributes (b) The ....

Jack A. Orenstein. "Spatial Query Processing in an Object-Oriented Database System". In Proceedings of the 1986.

....B is the number of pages that buffer can hold. For each such block, the second dataset is sequentially read one page at a time. This technique is called Block Nested Loop Join (NLJ) Sort Merge Join [5, 18, 33] sorts both of the datasets based on the join attribute and merges them. Orenstein [36] uses Zcurves to represent spatial objects. The bit representation of the Z value of objects is then used to find out whether the objects overlap. Hash Join (see [39] assigns objects in both datasets to buckets using the same hash functions. Later, the objects in the same bucket are joined. A ....

J.A. Orenstein. Spatial query processing in an object-oriented database system. In SIGMOD, pages 32(226, Washington, D.C., May 1986.

....Section 9 presents the 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 ....

....such block, the second dataset is sequentially read one page at a time. This technique is called Block Nested Loop Join. We will use NLJ to represent Block Nested Loop Join in this paper. Sort Merge Join [6, 19, 33] sorts both of the datasets based on the join attribute and merges them. Orenstein [36] uses Z curves to represent spatial objects. The bit representation of the Z value of objects is then used to find out whether the objects overlap. Hash Join (see [40] assigns objects in both datasets to buckets using the same hash functions. Later, the objects in the same bucket are joined. A ....

J.A. Orenstein. Spatial query processing in an objectoriented database system. In SIGMOD, pages 326--226, Washington, D.C., May 1986.

....of Possible Evaluation Algorithms We noted previously that time intervals lack a natural order. From this point of view, spatial join is similar because there is no natural order preserving spatial closeness. Previous work on spatial join may be categorized into three approaches. Early work [Ore86, OM88] used a transformation approach based on space filling curves, performing a sort merge join along the curve to solve the join problem. Most of the work falls in the index based approaches, utilizing spatial index structures such as the R tree [Gut84] R tree [SRF87] R tree [BKS90] ....

J. A. Orenstein. Spatial Query Processing in an Object-Oriented Database System. In Proceedings of the ACM SIGMOD Conference, 1986.

....is possible to decompose such queries in a sequence of some simple steps. There are wide range of methods for processing traditional joins [13] but they cannot be applied in spatial case efficiently because of not preserving spatial relations. Several methods using spatial indices were proposed [8, 7, 10, 9, 5], including those based on tree like access methods which usually assume existence indices for both participating data sets. The situation when this assumption is not true is considered in [10] where dynamically constructed index structure uses knowledge of the data sets are proposed to speed up ....

Orenstein J.A. Spatial query processing in an object-oriented database systems. In Proc. ACM SIGMOD Int. Conf. on Management of Data, pages 326--333, Washington, DC, 1986.

.... Object oriented databases SC tree [14] CH tree [14] H tree [16] Temporal databases Monotonic B tree [9] Bitemporal B tree [13] Transaction time [19] Valid time [20] String databases Prefix B tree 15] String B tree [12] Spatial databases location key based [1] UB tree [24] Z ordering [22] High dimensional databases iMinMax [21] iDistance [26] UB tree [24] Pyramid [6] Table 1: B tree based schemes. Query box I1 I4 (a) Z regions (b) Range search Figure 2: Z regions of UB tree, and its range search. Object representation and indexing structures are treated separately in ....

J. Orenstein. Spatial query processing in an object-oriented database system. In SIGMOD Conference 1986.

....good I O support has led to the development of a large number of external data structures, which do not have good theoretical worst case bounds but have good average case behavior for common spatial database problems. These includes the grid file [NHS] various quad trees [Sama, Samb] z orders [Ore] and other space filling curves, k d B trees [Rob] hB trees [LoS] cell trees [Gun] and various R trees [Gut, SRF] For these external data structures there has been a lot of experimentation but relatively little algorithmic analysis. Their average case performance (e.g. some achieve the ....

J. A. Orenstein, "Spatial Query Processing in an Object-Oriented Database System," Proc. ACM SIGMOD (1986), 326--336.

....in this paper we focus on range queries only. Several spatial access methods have been proposed forming the following classes: a) Methods that transform rectangles into points in a higher dimensionality space [38] b) Methods that use linear quad trees or, equivalently, the z ordering [39] or other space filling curves [40] and finally, c) Methods based on trees (k d trees [41] One of the most characteristic methods is the R tree [42] Methods referred to as metric trees are based on the idea of indexing using distance information. VantagePoint (VP) trees [43] and ....

Jack A. Orenstein. Spatial Query Processing in an Object Oriented Database System. In ACM Proceedings SIGMOD 86, pages 326--336, Washington, May 1986.

....in this paper we focus on range queries only. Several spatial access methods have been proposed, forming the following classes: a) Methods that transform rectangles into points in a higher dimensionality space [25] b) methods that use linear quad trees or, equivalently, the z ordering [26] or other space filling curves [27, 28] and finally, c) methods based on trees (k d trees [29] etc. One of the most characteristic approaches in the last class is the R tree [30] The R tree can be envisioned as an extension of the B tree for multidimensional objects. A geometric object is ....

Jack A. Orenstein. Spatial Query Processing in an Object Oriented Database System. In ACM Proceedings SIGMOD 86, pages 326--336, Washington, May 1986. 23

....the data point p j with the closest Z value from the B tree. Then, we compute the distance d between p i and p j and issue a range query centered at d with radius d. We check all the retrieved points and return the one with the shortest distance to the query point. In the PROBE project, Orenstein [6] constructed Z order B tree as the access method to process spatial queries, such as point and range query using spatial predicates of overlap. They showed that the approximate geometry, i.e. grid representation of spatial data, can be supported with very minor modification of the existing DBMS ....

....loss will we need to tolerate Z order B tree can help in all spatial queries, i.e. R trees are not necessary. Needed optimizer smarts include a preprocessor to transform spatial queries (e.g. MBR range queries) into a collection of queries (e.g. interval range queries) onto B tree. See [6, 2] for details. Also, a postprocessing is needed to assemble results of B tree interval range queries to answer original spatial query. Performance: there should not be any loss for point queries. Range query performance differences will be due to clustering efficiency and duplicate elimination (to ....

J. Orenstein. Spatial query processing in an object-oriented database system. In Proc. ACM SIGMOD Conf. pp. 326-336. 21, 1986.

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J. Orenstein: "Spatial query processing in an object-oriented database system", Proceedings of the 1986 ACM SIGMOD Conference, pp.326-336, Washington DC, 1986.

No context found.

J. Orenstein, "Spatial Query Processing in an Object-Oriented Database System," Proc. ACM SIGMOD, pp. 326-336, May 1986.

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Orenstein J.A. \Spatial Query Processing in an Object-Oriented Database System." Proceedings of ACM SIGMOD, pp. 326-336. Washington D.C, 1986

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Jack. A. Orenstein. Spatial query processing in an object-oriented database system. In Proceedings of ACM SIGMOD, pages 326--336, Washington D.C, 1986.

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J. A. Orenstein. Spatial query processing in an object-oriented database system. ACM SIGMOD, pages 326--336. ACM Press, 1986.

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A. Orenstein, Spatial query processing in an object oriented database system, In SIGMOD, Washington, D.C., pp. 326-236, 1986.

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J. A. Orenstein. Spatial query processing in an object-oriented database system. ACM SIGMOD, pages 326--336. ACM Press, 1986.

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A. Orenstein, Spatial query processing in an object oriented database system, In SIGMOD, Washington, D.C., pp. 326-236, 1986.

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J.A. Orenstein: "Spatial Query Processing in an Object Oriented Database System", Proceedings ACM SIGMOD Conference, pp.326-336, Washington, DC, 1986.

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Orenstein JA (1986) Spatial Query Processing in an Object-Oriented Database Systems. Proceedings of ACM SIGMOD Conference, Washington D.C., pp 326-333

No context found.

A. Orenstein, Spatial query processing in an object oriented database system, In SIGMOD, Washington, D.C., pp. 326-236, 1986.

No context found.

Orenstein J.A.: Spatial Query Processing in an Object-Oriented Database System. Proc. ACMSIGMOD Int. Conf. on Management of Data: 326-336, 1986.

No context found.

Jack. A. Orenstein. Spatial query processing in an object-oriented database system. In Proceedings of ACM SIGMOD, pages 326--336, Washington D.C, 1986.

No context found.

Orenstein, J. Spatial Query Processing in an Object-Oriented Database System. ACM SIGMOD Conf. 1986.

No context found.

Orenstein J.A.: Spatial Query Processing in an Object-Oriented Database System. Proc. ACMSIGMOD Int. Conf. on Management of Data: 326-336, 1986.

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