N. Beckmann, H.-P. Kriegel, R. Schneider, and B. Seeger: "The R # -tree: an Efficient and Robust Access Method for Points and Rectangles", Proc. ACM SIGMOD, pp. 322-331, Atlantic City, NJ, May 1990.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....with bold lines of the figure represent the buildings contained by the transformed query region. The computation time for transformation in this algorithm is almost negligible. And the performance of the algorithm is nearly dominated by delaunay triangulation and local query processing time [4]. Algorithm 3: Range Query Processing S T : other local database G = g s , g ) g s , g : corresponding control points on S S and S T a S = p S1 , p S2 , p S3 , p Sn ) query region given on S S b S # MakeNewPolygonWithIntersectingPoints (a S ,T S ) make a new polygon ....

N. Beckmann, H. P. Kriegel, R. Schneider, and B. Seeger, "The R*-Tree, An Efficient and Robust Access Method for Points and Rectangles", Proc. ACM SIGMOD, pp.322-331,

....that insertion of the new sibling may require further splits along the path of ancestors of the previously full node. Since splits are the sole mechanism for creating nodes, these splits shape the resulting R tree and affect its future query performance. Other dynamic approaches have been proposed [Bec90, Kam94, Sel87]. Guttman presented splitting algorithms with linear, quadratic, and exponential time complexity. He showed that the quadratic algorithm resulted in significantly better performance than the linear. Further more, he argued that the exponential algorithm was best, but due to its time complexity ....

....5.2 Results We first consider a comparison without the SHIFT heuristic. We compare Hilbert R trees (HILB) Guttman s quadratic (GUT) and Guttman s insertion algorithm using optimal splitting (OPT) Different values of node utilization affect overall R tree performance as previously shown in [Bec90]. To address this we consider minimum node occupancies of 0,10,20,40, and 50 of M . Therefore, we used the full version of the optimal bipartition algorithm with cardinality constraints. In Table 2 we present results for the tiger longbeach data set. There is a column for each buffer size and a ....

[Article contains additional citation context not shown here]

Beckmann, N., Kriegel, H.P., Schneider, R., Seeger, B., "The R -tree: an Efficient and robust Access Method for Points and Rectangles, " Proc. ACM SIGMOD, p. 323-331, May 1990.

....and that can give a quick response to range queries and nearest neighbor queries. We do not use SAMS as an index structure, but as a way of resolving data conflicts, which are explained in detail in Chapter 5. Several spatial access methods have been proposed, such as R tree and its variants [7, 25, 32, 38, 61], TV tree [50] X tree [8] Metric tree [65] MVP tree [10] and M tree [13] Those methods need a distance function that is used for comparing the data objects in the multi dimensional space. The distance function measures the (dis)similarity between two objects. It is defined for all ....

N. Beckmann, H. P. Kriegel, R. Schneider, and B. Seeger, "The R*-Tree: An Efficient and Robust Access Method for Points and Rectangles," Proceedings of the 1990.

....In this section, we present the performance of the new indexing techniques and compare them to existing techniques. The experiments reported are for two dimensional data; however, the techniques are not limited to two dimensions. The various indexing techniques were implemented as R trees [9] and tested on synthetic data. The dataset used consists of 100,000 objects composed of a collection of five normal distributions each with 20,000 objects. The mean values for the normal distribution are uniformly distributed and the standard deviation is 0.05 (the points are all in the unit ....

N. Beckmann, H. Kriegel, R. Schneider, and B. Seeger, "The R*-Tree: An Efficient and Robust Access Method for Points and Rectangles," Proc. ACM SIGMOD Int'l Conf. Management of Data, pp. 322-331, May 1990.

....with this work concentrated more on testing than on the production of a usable system. This section discusses several modifications that would be required to put the work in use. 5.2.1 Efficiency Improvement R tree has several improved variants, for example, R tree and R tree. R tree 69 [Beckmann 90] enhanced the original R tree in two major ways. First, it added the perimeter of the bounding regions as an important factor to the heuristics for node splitting. Second, it introduced the notion of forced reinsert to make the shape of the tree less dependent on the order of the insertion. ....

Norbert Beckmann et al., "The R*-tree: An efficient and robust access method for points and rectangles", Proceedings of the 1990 ACM SIGMOD International Conference on Management of Data, pp322-331

....subsequences that are similar to the query sequence. However, traditional database indexing techniques are inadequate for this purpose. There is currently much excellent work in indexing multidimensional data, including geometric hashing[25] grid based index structures[15] and the R tree family[9, 8] index structures. These spatial access methods, are designed to index unsequenced spatial objects. The order among the entities in the database is not taken into consideration when the index structures are created and hence no effective retrieval method in terms of subsequence matching of spatial ....

....4, we use our algorithm to solve subsequence matching problem for time series databases. Section 5 contains experiments that show the effectiveness of our algorithms. 2 Background Spatial Access Methods The R tree can be viewed as an extension of the B tree to multi dimensions. The R tree[8] improves the R tree by introducing a policy called forced reinseft: when a node overflows, it is not split right away, but a portion of the entries are removed and reinserted into the tree. The R tree also refines the node splitting policy of the R tree by taking overlapping area and region ....

[Article contains additional citation context not shown here]

Beckmann N., Kriegel H.-P., Schneider R., Seeger B.: "The R*-tree: an efficient and robust access method for points and rectangles", In SIGMOD 90, Atlantic City, N J, 1990, pp. 322-331.

....time selection operator sequence. The proposed approach may be combined with any algorithm for the implementation of the coalesce operator and allows the exploitation of index structures (i.e. queries retrieving tuples having valid times included in a given range amongst others [1] [3], 8] 12] 15] 18] 20] 21] 22] The remainder of this paper is organised as follows: in section 2 the optimisation scheme is described in detail and it is proved that the optimised execution scheme yields the same result with the coalesce valid time selection operator sequence. In ....

N. Beckmann, et al., "The R --tree: An Efficient and Robust Access Method for Points and Rectangles", Proceedings of the 1990.

....to inaccurate segmentation. To solve the second issue, many efforts have been made along either or both of the two directions: saving time and saving space. For the former, the efforts can be classified into two categories: one is using traditional tree structures [8, 23, 33] such as R tree [3] and M tree [5] the other is using statistical clustering [19, 35] For the latter, not much attention has been paid. One effort is the system proposed in [42] which is based on vector quantization. Few works have addressed both of the facets. One successful example is the VisualSEEk system ....

....algorithm to achieve an approximation. As a result, integrated region matching is not a metric, which makes most of the traditional indexing techniques impossible. 3. 2 Indexing Using Modified Inverted File As discussed in Section 1, many traditional tree structures such as R tree [10] R tree [3], M tree [5] and SR tree [17] have been introduced to index region based image retrieval systems. Unfortunately, the speed and accuracy of these algorithms degrade in high dimensional spaces [3, 17, 37] which is referred to as the curse of dimensionality. For example, the performance of R ....

[Article contains additional citation context not shown here]

Beckmann, N., Kriegel, H.-P., Schneider, R., Seeger, B., "The R*-tree: An efficient and robust access method for points and rectangles," Proc. ACM SIGMOD, pp. 322-331, Atlantic City, NJ, 23-25 May 1990.

....that no pair of records should be reported (if for example no record from the first relation qualifies) thus finishing the join fast. When considering the index used on each temporal re lation, various possibilities exist. One approach is to use traditional indices like B trees and R trees [5]. Such indices are widely implemented and lead to straightforward synchronized joins. However, such joins suffer from the ineffectiveness of the B tree and (to a lesser extent) the R tree on clustering temporal data. Temporal data is inherently multidimensional having typically long intervals on ....

....However, such joins suffer from the ineffectiveness of the B tree and (to a lesser extent) the R tree on clustering temporal data. Temporal data is inherently multidimensional having typically long intervals on the time dimension. Even the R tree (and its most efficient variation, the R tree [5]) are known to be problematic in clustering long intervals [12, 24] Better clustering is achieved by temporal access methods that create many copies for records with long time intervals [17, 3] This leads to fast processing of range interval selection queries, but record copies can greatly ....

N. Beckmann, H. Kriegel, R. Schneider and B. Seeger, "The R* tree: An Efficient and Robust Access Method for Points and Rectangles", Proc. of SIGMOD, 1990.

No context found.

Beckmann N., Kriegel H.-P., Schneider R., Seeger B.: `The R*-Tree: An Efficient and Robust Access Method for Points and Rectangles', Proc. ACMSIGMOD Int. Conf. on Management of Data, Atlantic City, NJ, 1990, pp. 322-331.

No context found.

Beckmann N., Kriegel H.-P., Schneider R., Seeger B.: `The R*-tree: An Efficient and Robust Access Method for Points and Rectangles', Proc. ACM SIGMOD Int. Conf. on Management of Data, Atlantic City, NJ, 1990, pp. 322-331.

No context found.

Beckmann N., Kriegel H.-P., Schneider R., Seeger B.: "The R*-tree: An Efficient and Robust Access Method for Points and Rectangles", Proc. ACM SIGMOD Int. Conf. on Management of Data (SIGMOD '90), Atlantic City, NJ, ACM Press, New York, 1990, pp. 322?331.

No context found.

N. Beckmann, H.-P. Kriegel, R. Schneider, and B. Seeger: "The R # -tree: an Efficient and Robust Access Method for Points and Rectangles", Proc. ACM SIGMOD, pp. 322-331, Atlantic City, NJ, May 1990.

No context found.

N. Beckmann, H.-P. Kriegel, R. Schneider, and B. Seeger, "The R -tree: an efficient and robust access method for points and rectangles," in Proc. ACM SIGMOD Int. Conf. Management of Data, Atlantic City, NJ, 1990, pp. 322--331.

No context found.

N. Beckmann, H. Kriegel, R. Schneider, and B. Seeger, "The R*-tree: An Efficient and Robust Access Method for Points and Rectangles", Proc. ACM SIGMOD Int. Conf. on Management of Data, pp. 322-331, 1990.

No context found.

N. Beckmann, H.-P. Kriegel, R. Schneider, and B. Seeger, "The r -tree: An efficient and robust access method for points and rectangles," in Proc. ACM SIGMOD Int. Conf. Management of Data, May 1990, pp. 322--331.

No context found.

N. Beckmann, H.-P. Kriegel, R. Schneider, and B. Seeger: "The R # -tree: an Efficient and Robust Access Method for Points and Rectangles", Proc. ACM SIGMOD, pp. 322-331, Atlantic City, NJ, May 1990.

No context found.

N. Beckmann and H.-P. Kriegel, "The R # -Tree: An Efficient and Robust Access Method for Points and Rectangles," Proc. ACM SIGMOD Conf. Management of Data, pp. 322-331, 1990.

No context found.

N. Beckmann, H.-P. Kriegel, R. Schneider, and B. Seeger, "The r*-tree: an efficient and robust access method for points and rectangles," in Proceedings of the

No context found.

N. Beckmann, H. Kriegel, R. Schneider, and B. Seeger, "The R*Tree: An Efficient and Robust Access Method for Points and Rectangles," Proc. ACM SIGMOD Int'l Conf. Management of Data, pp. 322-331, May 1990.

No context found.

N. Beckmann, H. Kriegel, R. Schneider, and B. Seeger, "The R*Tree: An Efficient and Robust Access Method for Points and Rectangles," Proc. ACM SIGMOD Int'l Conf. Management of Data, pp. 322-331, May 1990.

No context found.

N. Beckman, H.-P. Kriegel, R. Schneider, and B. Seeger, "The R*-tree: An Efficient and Robust Access Method for Points and Rectangles," Proc. ACM SIGMOD Intl. Conf. on

No context found.

N. Beckmann, H.P. Kriegel, R. Schneider, and B. Seeger, "The R*-Tree: An Efficient and Robust Access Method for Points and Rectangles," Proc. ACM SIGMOD Int'l Conf. Management of Data, pp. 322-331, May 1990.

No context found.

N. Beckmann, H.P. Kriegel, R. Schneider, B. Seeger: "The R*-Tree: An Efficient and Robust Access Method for Points and Rectangles". SIGMOD Conference 1990.

No context found.

N. Beckmann, H. Kriegel, R. Schneider and B. Seeger, "The R* tree: An Efficient and Robust Access Method for Points and Rectangles", Proc. of SIGMOD, 1990.

First 50 documents  Next 50

Online articles have much greater impact   More about CiteSeer.IST   Add search form to your site   Submit documents   Feedback  

CiteSeer.IST - Copyright Penn State and NEC