J. T. Robinson, "The K-D-B-tree: a search structure for large multidimensional dynamic indexes," in Proc. ACM SIGMOD Int. Conf. Management of Data, Ann Arbor, MI, 1981, pp. 10--18.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....approach, the data set is organized such that only a partial representation (e.g. 2 out of d dimensions in dimensionality reduction) of each object is examined. Index structures: Several index structures have been proposed for retrieval of multidimensional data. Examples include kdb trees [39], hB tree [34] R tree [24] R tree [4] SS tree [47] TV tree [33] X tree [9] Pyramid Technique [8] Hybrid Tree [12] Various algorithms for similarity searching have been developed in conjunction with these indexing mechanisms. The techniques developed for these structures mostly focus on ....

J. T. Robinson. The kdb-tree: A search structure for large multi-dimensional dynamic indexes. In Proc. ACM SIGMOD Int. Conf. on Management of Data, pages 10--18, 1981.

....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 desirable static query I O time of O(log B n t=B) on ....

J. T. Robinson, "The K-D-B Tree: A Search Structure for Large Multidimensional Dynamic Indexes," Proc. ACM SIGMOD (1984), 10--18.

....there have been proposals for GIS applications to handle queries with more complex and accurate structures, such as polygons. Numerous index structures have been developed to facilitate range searching in two and higher dimensions including grid files [NHK84] quadtrees [Sam89] kdb trees [Rob81] hB trees [LS90] R trees and variants [Gut84, BKSS90, BKK96] Another very important class of queries in applications that involve spatial data is that of nearest neighbor (NN) queries [RKV95] Similar to range queries, nearest neighbor queries are also commonly used in spatial applications. ....

J. T. Robinson. The kdb-tree: A search structure for large multi-dimensional dynamic indexes. In Proc. ACM SIGMOD Int. Conf. on Management of Data, pages 10--18, 1981.

....information technology. In these applications, the data objects are represented as two dimensional feature vectors, and the similarity between objects are defined by a distance function between corresponding feature vectors. Several index structures have been proposed for retrieval of spatial data [20, 11, 16, 19, 12, 1]. The common approach is to group the objects according to their spatial locations and store the created groups as pages in physical storage. Most of the approaches in the literature for indexing spatial data are based on clustering data points with a rectangular organization [20, 11, 12, 1] ....

J. T. Robinson. The kdb-tree: A search structure for large multi-dimensional dynamic indexes. In Proc. ACM SIGMOD Int. Conf. on Management of Data, pages 10--18, 1981.

....the record, whose key is f point, into the index. Figure 4. The index building algorithm BuildIndex. Dual Match has additional advantages: 1) it can use point access methods (PAMs) as the index, and 2) the index creation is very fast. Multidimensional index methods can be categorized into PAMs [13, 14, 16, 17] that store points and spatial access methods (SAMs) 3, 9, 15] that store spatial objects [8] Since Dual Match stores points, it can use a PAM as the index with a flexibility of using various multidimensional indexes of differing characteristics. Dual Match can create the index much faster than ....

Robinson, T. J., "The K-D-B Tree: A Search Structure for Large Multidimensional Dynamic Indexes," In Proc. Int'l Conf. on Management of Data, ACM SIGMOD, Ann Arbor, Michigan, pp. 10-18, Apr. 1981.

....group comprises all index structures that organize the data in fixed capacity pages with a given storage utilization. Prominent members of this group are the R tree [15] and its variants (R tree, R tree) the Xtree [7] the SS tree [35] the SR tree [20] the M tree [11] the k d B tree [29], and the grid file [27] An example for an index structure not contained in this group is the VA file [32] since it does not organize points in pages of fixed capacity. 5. EXPERIMENTAL RESULTS In order to get an impression of the real running times of our algorithms, we implemented all of ....

J. T. Robinson. The kdb-tree: A search structure for large multi-dimensional dynamic indexes. In Proc. ACM SIGMOD Int. Conf. on Management of Data, pages 10--18, 1981.

....Given these image content representation, difference measure, and query types, we will propose a new indexing scheme, called the HG tree, for efficient image retrieval. It is a multidimensional point access method (PAM) We distinguish basically between the point access methods, such as K D B tree [25] and buddy tree [28] and the spatial access methods (SAMs) such as R tree [11] and R tree [1] which are designed to handle multidimensional point and spatial (non point) data, respectively. Spatial data are data pertaining to the space occupied by objects in a database. They consists of lines, ....

....to all two classifying properties, that is, their directory regions are hyper rectangular and complete. They perform rather efficiently for uniform and uncorrelated data. However, for highly correlated data their 3 A property rectangular complete grid file [19] BMEH tree [21] K D B tree [25] , MB tree [32] C2 BANG file [8] hB tree [16] BV tree [9] zkdb tree [20] C3 buddy tree [28] multilevel grid file [29] G tree [15] C4 HG tree Table 1 Classification of multidimensional PAMs performance degenerates. Grid files partition a data space into a grid structure by ....

J.T. Robinson, "The K-D-B-Tree: A Search Structure for Large Multidimensional Dynamic Indexes," Proceedings of the ACM SIGMOD Int'l. Conf. on Management of Data, pp. 1018, 1981. 21

....use other index structures for visual feature based indexes. In fact, many other image retrieval systems have used some other index structures. The QBIC system adopted the R tree [9] as an index structure. Petrakis and Faloutsos [10] used R tree [11] Mehrotra and Gary [12] used the K D B tree [13]. The systems, CAFIIR [4] and STAR [5] and Zhang and Zhong [14] employed the iconic index tree based on the Self Organizing Map (SOM) 15] Each of these methods has not only its own advantages, but also has some limitations. Compared with the other index structures, the performance of the ....

J. T. Robinson, "The K-D-B-Tree: A Search Structure for Large Multidimensional Dynamic Indexes," Proceedings of the ACM SIGMOD International Conference on Management of Data, 1981, 10-18.

....indexing mechanisms that support nearest neighbor search over multidimensional feature vectors. Several indexing mechanism suited for multimedia features (referred to as the F index or the feature index [15] have been proposed recently (e.g. R trees [22] R trees [41] R trees [2] k d B trees [34], hB trees [11] TV trees [27] SS trees [52] vp trees [8] M trees [9] Any such indexing mechanism can be used for indexing the feature vectors. We have developed a hybrid multidimenional index mechanism that combines the advantages of the two broad classes of multidimensional index ....

 J.T. Robinson, "The K-D-B-Tree: A Search Structure for Large Multidimensional Dynamic Indexes," Proc. SIGMOD ACM, 1981.

....are typically represented by multi dimensional feature vectors. A similarity function between these feature vectors is defined and is used as a measure for the similarity between the corresponding data objects. Several indexing techniques have been proposed for multidimensional Euclidean spaces [20, 29, 28, 25, 21, 2, 34, 24, 5, 4]. Most of these structures are optimized specifically for point, range, and nearest neighbor queries with Euclidean distance as the similarity metric between feature vectors. Conical queries are a novel type of query with an increasing number of applications. Traditional index structures and ....

J. T. Robinson. The kdb-tree: A search structure for large multi-dimensional dynamic indexes. In Proc. ACM SIGMOD Int. Conf. on Management of Data, pages 10--18, 1981.

....there have been proposals for GIS applications to handle queries with more complex and accurate structures, such as polygons. Numerous index structures have been developed to facilitate range searching in two and higher dimensions including grid files [NHK84] quadtrees [Sam89] kdb trees [Rob81] hB trees [LS90] R trees and variants [Gut84, BKSS90, BKK96] Another very important class of queries in applications that involve spatial data is that of nearest neighbor (NN) queries [RKV95] Similar to range queries, nearest neighbor queries are also commonly used in spatial applications. ....

J. T. Robinson. The kdb-tree: A search structure for large multi-dimensional dynamic indexes. In Proc. ACM SIGMOD Int. Conf. on Management of Data, pages 10--18, 1981. 18

....of a query. Instead of n objects, the query retrieves a set of s objects, which is a subset of these n objects. The better the index structure, the smaller the size of s. There have been several approaches to organize and partition a multi dimensional data set for indexing including kdb trees [37], hB tree [31] R tree [23] R tree [5] SS tree [43] TV tree [28] X tree [8] Pyramid Technique [7] Hybrid Tree [12] There are also techniques that are proposed to reduce the size of the retrieved set in multiple disk architectures [16, 19, 20] All these index structures focus mainly on ....

J. T. Robinson. The kdb-tree: A search structure for large multi-dimensional dynamic indexes. In Proc. ACM SIGMOD Int. Conf. on Management of Data, pages 10--18, 1981.

....multidimensional data. In these applications, the data objects are represented as twodimensional feature vectors, and the similarity between objects are defined by a distance function between corresponding feature vectors. Several index structures have been proposed for retrieval of spatial data [20, 11, 16, 19, 12, 1]. The common approach is to group the objects according to their spatial locations and store the created groups as pages in physical storage. Most of the approaches in the literature for indexing spatial data are based on clustering data points with a rectangular organization [20, 11, 12, 1] ....

J. T. Robinson. The kdb-tree: A search structure for large multi-dimensional dynamic indexes. In Proc. ACM SIGMOD Int. Conf. on Management of Data, pages 10--18, 1981.

....of a query. Instead of n objects, the query retrieves a set of s objects, which is a subset of these n objects. The better the index structure, the smaller the size of s. There have been several approaches to organize and partition a multi dimensional data set for indexing including kdb trees [35], hB tree [29] R tree [22] R tree [5] SS tree [41] TV tree [26] X tree [8] Pyramid Technique [7] Hybrid Tree [12] There are also techniques that are proposed to reduce the size of the retrieved set in multiple disk architectures [15, 18, 19] All these index structures focus mainly on ....

J. T. Robinson. The kdb-tree: A search structure for large multi-dimensional dynamic indexes. In Proc. ACM SIGMOD Int. Conf. on Management of Data, pages 10--18, 1981.

....set of rectangles R 1 , R 2 , R k , where: 3 k R = U R i , k 1 i=1 so that each R i , 1 i k, intersects with exactly one disjoint region. These rectangles are represented in a file and may be organized by any multidimensional PAM, like the grid file [Niev84] or the K DB tree [Robi81]. Examples of SAMs using the clipping technique are the R tree [Sell87] and the Cell tree [Gunt89] Since these structures avoid overlapping regions the search operation is very efficient. Additionally, the properties of the underlying PAM are inherited. However, a drawback is obviously the ....

....and compare its efficiency to that of popular SAMs adopting other techniques, such as the R tree [Gutt84] and R tree [Back90] At the moment, all tests are confined in the two dimensional space. The structure chosen to play the role of the underlying PAM in the experiments is the KDB tree [Robi81]. The KDB tree is a very efficient multidimensional PAM which combines the properties of both KD tree [Bent75] and B tree [Come79] A Composite KDB tree has been implemented to support the object overhead elimination for data sets consisting of various MBR sizes. What differentiates it from ....

J.T. Robinson: `The K-D-B-tree: A Search Structure for Large Multidimensional Dynamic Indexes', Proceedings of the ACM SIGMOD Conference on Management of Data, Ann Arbor, Michigan, 10-18, 1981.

....with a specific resolution of the space, and use a one dimensional access method with this ordering. They 2 perform transformations on higher order keys to impose total ordering. Example methods include Z ordering [32] and Hilbert Curves [2, 11, 22] Multidimensional B trees [35] and K dB trees [33] establish a correspondence between the levels of the index and dimensions. These approaches limit the opportunities for clustering according to connectivity. Other spatial access methods capture the isotropic nature of proximity by recursively dividing the space, using a splitting rule to ....

J.T. Robinson. "The K-D-B-tree: A Search Structure for Large Multidimensional Dynamic indexes". In Proc. of SIGMOD Intl Conference on Management of Data, pages 10--18. ACM, 1981.

No context found.

J. T. Robinson, "The K-D-B-tree: a search structure for large multidimensional dynamic indexes," in Proc. ACM SIGMOD Int. Conf. Management of Data, Ann Arbor, MI, 1981, pp. 10--18.

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J.T. Robinson, "The K-D-B Tree: A Search Structure for Large Multidimensional Dynamic Indexes", Proc. ACM SIGMOD Int. Conf. on Management of Data, pp. 10-18, 1981.

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J.T. Robinson, "The KDB tree: A search structure for large multidimensional dynamic indexes", in Proceedings of ACM SIGMOD, Ann Arbor, April, (1981).

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J.T. Robinson, "The K-D-B Tree: A Search structure for Large Multidimensional Dynamic Indexes", Proc. of ACM SIGMOD Conference, pp. 10-18, April 1981.

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J.T. Robinson, "The kdb-Tree: A Search Structure for Large MultiDimensional Dynamic Indexes," Proc. ACM SIGMOD Int'l Conf. Management of Data, pp. 10-18, 1981.

No context found.

J.T. Robinson, "The kdb-Tree: A Search Structure for Large MultiDimensional Dynamic Indexes," Proc. ACM SIGMOD Int'l Conf. Management of Data, pp. 10-18, 1981.

No context found.

J. T. Robinson. The kdb-tree: A search structure for large multi-dimensional dynamic indexes. In Proc. ACM SIGMOD Int. Conf. on Management of Data, pages 10--18, 1981.

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J.T. Robinson. The kdB Tree: A Search Structure for Large Multidimensional Dynamic Indexes. In Proc. ACM SIGMOD Int. Conf. on Management of Data, pages 10--18, 1981.

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J. T. Robinson, "The K-D-B-tree: a search structure for large multidimensional dynamic indexes", In Proc. of ACM SIGMOD, Ann Arbor, April 1981.

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