L. Arge, O. Procopiuc, S. Ramaswamy, T. Suel, and J. Vitter. Scalable Sweeping-Based Spatial Join. In Proc. of the Int'l Conference on Very Large Databases, 1998.  Home/Search   Document Details and Download   Summary   Related Articles   Check

....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 ....

Lars Arge, Octavian Procopiuc, Sridhar Ramaswamy, Torsten Suel, and Jeffrey. S. Vitter. Scalable sweepingbased spatial join. In Proceedings of the 24th VLDB Conference, pages 259--270, New York, USA, June 1998.

....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 ....

.... Algorithm 3: AM KDJ: Adaptive Multi Stage K Distance Join Algorithm (Compensation Stage) 1: insert all elements in QC into 2: while jAnswerSetj k and QM 6= do 5: else CompensatePlaneSweep(c) procedure CompensatePlaneSweep(hl; ri) 6: L f entries of l sorted in Stage Oneg; fL[1]; L[2] L[jLj]g 7: R f entries of r sorted in Stage Oneg; fR[1] R[2] R[jRj]g 9: n a node with the min axis value 2 L [ R; n becomes an anchor. 11: L L fng; R fnode list in R not paired with n in the Stage One g; f R[n:compensate] R[n:compensate 1] ....

[Article contains additional citation context not shown here]

Lars Arge, Octavian Procopiuc, Sridhar Ramaswamy, Torsten Suel, and Jeffrey. S. Vitter. Scalable sweepingbased spatial join. In Proceedings of the 24th VLDB Conference, pages 259--270, New York, USA, June 1998.

....lter step and a re nement step [19] to eciently process complex spatial data types such as point collections. In the lter step, the spatial objects are represented by simpler approximations such as the MBR (Minimum Bounding Rectangle) There are several well known algorithms, such as plane sweep [3], space partition [13] and tree matching [14] which can then be used for computing the spatial join of MBRs using the overlap relationship; the answers from this test form the candidate solution set. In the re nement step, the exact geometry of each element from the candidate set and the exact ....

L. Arge, O. Procopiuc, S. Ramaswamy, T. Suel, and J. Vitter. Scalable SweepingBased Spatial Join. In Proc. of the 24th Int'l Conf. on VLDB, 1998.

....step and a refinement step [19] to e#ciently process complex spatial data types such as point collections. In the filter step, the spatial objects are represented by simpler approximations such as the MBR (Minimum Bounding Rectangle) There are several well known algorithms, such as plane sweep [3], space partition [13] and tree matching [14] which can then be used for computing the spatial join of MBRs using the overlap relationship; the answers from this test form the candidate solution set. In the refinement step, the exact geometry of each element from the candidate set and the exact ....

L. Arge, O. Procopiuc, S. Ramaswamy, T. Suel, and J. Vitter. Scalable SweepingBased Spatial Join. In Proc. of the 24th Int'l Conf. on VLDB, 1998.

....to the same locations are then checked to find the candidate objects that may contribute to the final join. Next, objects are read from the first dataset into buffer, and the second dataset is sequentially scanned to find the actual join results. Scalable Sweeping based Spatial Join (SSSJ) [3] starts by sorting the objects according to their lower values in one of the dimensions in ascending order. Later, the objects are scanned sequentially in this order. For each object, the intersecting objects are found by scanning the other dataset. Gurret and Rigaux [19] show that the performance ....

L. Arge, O. Procopiuc, S. Ramaswamy, T. Suel, and J. S. Vitter. Scalable sweeping-based spatial join. In VLDB, New York, 1998.

....lter step and a re nement step [24] to eciently process complex spatial data types such as point collections. In the lter step, the spatial objects are represented by simpler approximations such as the MBR (Minimum Bounding Rectangle) There are several well known algorithms, such as plane sweep [4], space partition [15] and tree matching [17] which can then be used for computing the spatial join of MBRs using the overlap relationship; the answers from this test form the candidate solution set. In the re nement step, the exact geometry of each element from the candidate set and the exact ....

L. Arge, O. Procopiuc, S. Ramaswamy, T. Suel, and J. Vitter. Scalable Sweeping-Based Spatial Join. In Proc. of the Int'l Conference on Very Large Databases, 1998.

....nement step [24] to eciently process complex spatial data types such as point collections in a row instance. In the lter step, the spatial objects are represented by simpler approximations such as the MBR Minimum Bounding Rectangle. There are several well known algorithms, such as plane sweep [4], space partition [15] and tree matching [19] which can then be used for computing the spatial join of MBRs using the overlap relationship; the answers from this test form the candidate solution set. In the re nement step, the exact geometry of each element from the candidate set and the exact ....

L. Arge, O. Procopiuc, S. Ramaswamy, T. Suel, and J. Vitter. Scalable Sweeping-Based Spatial Join. In Proc. of the Int'l Conference on Very Large Databases, 1998.

....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 ....

....smaller partitions. In the second phase, duplicate objects are eliminated by sorting the candidate objects. Next, as many objects are read from first dataset into buffer, and the second dataset is sequentially scanned to find the actual join results. Scalable Sweeping based Spatial Join (SSSJ) [3] starts by sorting the objects in according to their lower values in one of the dimensions in ascending order. Later, the objects are scanned sequentially in this order. For each object, the intersecting objects are found by scanning the other dataset. If the search structure becomes too large, ....

L. Arge, O. Procopiuc, S. Ramaswamy, T. Suel, and J. S. Vitter. Scalable sweeping-based spatial join. In VLDB, New York, 1998.

....of generalized apriori gen and then neighbor based pruning like in generation of co location rules of size 3 or more. The spatial inner join of the instances of all spatial features will produce pairs of instances with 12 neighbor relation R. A minor modi cation of a sweeping based spatial join [4], which eliminates pairs of instances in a neighborhood with the same spatial feature type will produce all table instances of size 2 co locations. We order the row instances in the table instance of each co location in increasing lexicographic order. Finally, we compute the participation index of ....

.... total number of candidate prevalent co locations of size i 1 in iteration i after generation of table instances and neighborhood based pruning in step 5 (Geometric lter) locations of size 2: cost step 1 = O(N ) Step 2 uses an ecient spatial join algorithm such as a sweeping based algorithm [4] to produce candidate prevalent co locations of size 2, summarize row instances to their corresponding co location table, order co locations and table instances of each co location in lexicographic order, and then perform prevalence based pruning. Let the average size of the table instances before ....

L. Arge, O. Procopiuc, S. Ramaswamy, T. Suel, and J. Vitter. Scalable SweepingBased Spatial Join. In Proc. of the Int'l Conference on Very Large Databases, 1998.

....can be applied) For the nonindexed environment, an initial sorting is sufficient. When the R tree index exists, it is exploited to directly extract the data in sorted order according to the plane sweep direction. This algorithm is an extension to the scalable sweepingbased spatial join (SSSJ) [1] to the case of indexed inputs. 4 Synchronized GT Join Algorithms 4.1 GT Join based on Traditional Indices A one dimensional index like the B tree, clusters data primarily on a single attribute. Consider first a B tree that clusters on the interval start time. Because of the transaction time ....

L. Arge, O. Procopiuc, S. Ramaswamy, T. Suel and J. Vitter, "Scalable Sweeping-Based Spatial Join", Proc. of VLDB, 1998.

....TEP GRACE partitioning on time explicit attributes Hybrid timestamp explicit partitioning TEP H Hybrid partitioning on time explicit attributes Nested loop NL Exhaustive matching sweep algorithm which can be thought of as the spatial equivalent of the sort merge algorithm. Arge et al. APR98] introduced a highly optimized implementation of the sweeping based algorithm that first sorts the data along the vertical axis, after which it partitions the input into a number of vertical strips. Data in each strip can then be joined by an internal plane sweep algorithm. The above non index ....

L. Arge, O. Procopiuc, S. Ramaswamy, T. Suel, and J. .S. Vitter. Scalable Sweeping-based Spatial Join. In Proceedings of the International Conference on Very Large Databases, pages 570--581, 1998.

....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. They assume that each data set is indexed using an R tree (or similar index ....

L. Arge, O. Procopiuc, S. Ramaswamy, T. Suel, and J. S. Vitter. Scalable sweeping-based spatial join. In Proceedings of the Twenty-fourth International Conference on Very Large Databases, pages 570--581, 1998.

....way as the intersection join. There have been developed various methods for processing spatial joins for spatial data using approximate geometry [13, 14] two R trees [3] PMR quadtrees [5] seeded trees when one [9] or none [10] of the data sets does not have a spatial index, spatial hashing [1, 11, 15], or sort merge join [8] In this paper, we make the assumption that our spatial information system keeps non regional data in R trees or R trees and regional data in Linear Region Quadtrees, while users pose queries that involve both these two kinds of data. For example, the non regional data ....

L. Arge, O. Procopiuc, S. Ramaswamy, T. Suel and J.S. Vitter, "Scalable SweepingBased Spatial Join", Proceedings of the 24th VLDB conference, New York, 1998, pp. 570-581.

....external memory but without support for parallel independent disks. These include distribution sweeping and a variety of geometric algorithms based upon distribution sweeping [10] trapezoidal decomposition, triangulation of a simple polygon, red blue line intersection in GIS [3] and spatial join [2]. In many cases, the algorithms can be adapted to use parallel disks on an ad hoc basis by applying techniques from the previously developed parallel disk sorting algorithms, such as those by Vitter and Shriver [21] Nodine and Vitter [14, 15] and Dehne et al. 7, 8] but in practice these ....

....blocks are needed. Relevant algorithms include orthogonal segment line segment intersection, all nearest neighbors of a point set and a variety of other geometric algorithms [10] trapezoidal decomposition, triangulation of a simple polygon, red blue line intersection in GIS [3] and spatial join [2]. 8 Discussion and Conclusions In this paper we showed that randomized cycling distribution sort RCD is theoretically optimal for sorting with parallel disks, and it is practical for implementation. A detailed implementation is being pursued as part of a parallel disk environment we are ....

L. Arge, O. Procopiuc, S. Ramaswamy, T. Suel, and J. S. Vitter. Scalable sweeping-based spatial join. In Proceedings of the International Conference on Very Large Databases, volume 24, pages 570-581, New York, August 1998.

....external memory but without support for parallel independent disks. These include distribution sweeping and a variety of geometric algorithms based upon distribution sweeping [10] trapezoidal decomposition, triangulation of a simple polygon, red blue line intersection in GIS [3] and spatial join [2]. In many cases, the algorithms can be adapted to use parallel disks on an ad hoc basis by applying techniques from previous parallel disk sorting algorithms, such as those by Vitter and Shriver [19] Nodine and Vitter [13, 14] and Dehne et al. 7, 8] but in practice these techniques are often ....

....are needed. Relevant algorithms include orthogonal segment line segment intersection, all nearest neighbors of a point set and a variety of other geometric algo rithms [10] trapezoidal decomposition, triangulation of a simple polygon, red blue line intersection in GIS [3] and spatial join [2]. 8 Conclusions In this paper we showed that randomized cycling distribution sort RCD is theoretically optimal for sorting with parallel disks, and it is practical for implementation. A detailed implementation is being pursued as part of a parallel disk environment we are developing. We ....

L. Arge, O. Procopiuc, S. Ramaswamy, T. Suel, and J. S. Vitter. Scalable sweeping-based spatial join. In Proceedings of the th International Conference on Very Large Databases, pages 570-581, New York, August 1998.

No context found.

L. Arge, O. Procopiuc, S. Ramaswamy, T. Suel, and J. Vitter. Scalable Sweeping-Based Spatial Join. In Proc. of the Int'l Conference on Very Large Databases, 1998.

No context found.

L. Arge and O.Procopiu and S.Ramaswamy T.Suei J.S.Vitter. Scalable Sweeping-Based Spatial Join. In VLDB, 1998.

No context found.

L. Arge, O. Procopiuc, S. Ramaswamy, T. Suel, and J. S. Vitter. Scalable Sweeping-Based Spatial Join. In VLDB, 1998.

No context found.

L. Arge, O. Procopiuc, S. Ramaswamy, T. Suel, and J. S. Vitter. Scalable Sweeping-Based Spatial Join. In VLDB, 1998.

No context found.

L. Arge, O. Procopiuc, S. Ramaswamy, T. Suel, and J. Vitter. Scalable Sweeping-Based Spatial Join. In Proc. of the Int'l Conference on Very Large Databases, 1998.

No context found.

L. Arge, O. Procopiuc, S. Ramaswamy, T. Suel and J. Vitter, \Scalable Sweeping-Based Spatial Join", Proc. of VLDB, 1998. 10

No context found.

L. A. Arge, O. Procopiuc, S. Ramaswamy, T. Suel, and J. S. Vitter. Scalable sweepingbased spatial join. In Proc. 24th Int. Conf. Very Large Data Bases, VLDB, pages 570--581, 24-- 27 Aug. 1998.

No context found.

Arge, L., Procopiuc, O, Ramaswamy, S., Suel, T., Vitter, J. S. Scalable Sweeping-Based Spatial Join. VLDB, 1998.

No context found.

L. Arge, O. Procopiuc, S. Ramaswamy, T. Suel, and J.S. Vitter, "Scalable Sweeping-Based Spatial Join," Proc. Very Large Data Base Conf., pp. 570-581, Aug. 1998.

No context found.

L. Arge, O. Procopiuc, S. Ramaswamy, T. Suel, and J. Vitter. Scalable sweeping-based spatial join. In Proc. Int. Conf. on Very Large Data Bases, 1998.

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