A file structure supporting traversal recursion

Larson, Paul A.; Deshpande, V.

doi:10.1145/66926.66949

Cited by 18 publications

(21 citation statements)

References 4 publications

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“…In the connectivity-based approaches [17,18,22,23,26], the connectivity relationship is embedded in graph representations of spatial networks. Based on this fact, efficient access methods are proposed for directed network graphs with no cycles [27][28][29][30] and with limited cycles [31]. However, as these proposals rely on the total ordering of the graph vertices, they do not accurately model all kinds of spatial networks including road networks.…”

Section: Related Workmentioning

confidence: 99%

Clustering spatial networks for aggregate query processing: A hypergraph approach

Demir

Aykanat

Cambazoğlu

2008

Information Systems

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In spatial networks, clustering adjacent data to disk pages is highly likely to reduce the number of disk page accesses made by the aggregate network operations during query processing. For this purpose, different techniques based on the clustering graph model are proposed in the literature. In this work, we show that the state-of-the-art clustering graph model is not able to correctly capture the disk access costs of aggregate network operations. Moreover, we propose a novel clustering hypergraph model that correctly captures the disk access costs of these operations. The proposed model aims to minimize the total number of disk page accesses in aggregate network operations. Based on this model, we further propose two adaptive recursive bipartitioning schemes to reduce the number of allocated disk pages while trying to minimize the number of disk page accesses. We evaluate our clustering hypergraph model and recursive bipartitioning schemes on a wide range of road network datasets. The results of the conducted experiments show that the proposed model is quite effective in reducing the number of disk accesses incurred by the network operations. r

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Section: Related Workmentioning

confidence: 99%

Clustering spatial networks for aggregate query processing: A hypergraph approach

Demir

Aykanat

Cambazoğlu

2008

Information Systems

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“…[1,2,21,22,25]). We do not presuppose specific implementations of transitive closure algorithms on each processor, instead we may use any favourable central algorithm.…”

Section: )mentioning

confidence: 99%

Complex transitive closure queries on a fragmented graph

Houstsma

Apers²,

Ceri

1990

Icdt '90

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In this paper we study the reformulation of transitive closure queries on a fragmented graph. We split a query into several subqueries, each requiring only a fragment of the graph. We prove this reformulation to be correct for shortest path and bill of material queries. Here we describe the reformulation for an abstract graph, elsewhere we have described an actual implementation of our approach and some promising simulation results.We view the study of distributed computation of transitive closure queries as a result of the trend towards distributed computation. First selections were distributed to fragments of a relation, then fragmentation was used to compute joins in a distributed way, and now we are studying distributed computation of transitive closure queries. This should result in a deeper insight into the use and possible benefit of parallelism. Our work may be used in ordinary distributed databases as well as advanced multiproeessor database machines, such as PRISMA.Although this research was started to efficiently use distributed computation, it turns out to be beneficiary in a central environment as well. This is due to the introduction of extra selections, stemming from an appropriate fragmentation. This leads to extra focus on relevant data.

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“…The function GetFirst retrieves the node with lowest OrderKey value from the queue and also deletes it from the queue. Clearly, the execution of this algorithm requires only a single-pass scan of the relation if it is clustered topologically as suggested by Larson and Deshpande [5]. That is, the tuples are clustered in the same order that they are traversed by the algorithm.…”

Section: Endwhile; Endfinsuccessorsmentioning

confidence: 99%

“…Larson and Deshpande [5] proposed an elegant storage structure called topological clustering. The objective of topological clustering is to efficiently support the following operations on large directed acyclic graphs: 0 Finding all successors (predecessors) of a given set of start nodes.…”

Section: Topological Clusteringmentioning

confidence: 99%

“…This scheme, however, does not allow for arbitrary insertion and deletion of edges, or for arbitrary deletion of nodes. In order to overcome this problem, Larson et al [5], proposed a new file structure, based on topological ordering, to support traversal recursion. The nodes of a graph are stored in topological order (i.e., for every node x, all successors of x occur after x in the ordering file).…”

Section: Introductionmentioning

confidence: 99%

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