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17
External Memory Data Structures
, 2001
"... In many massive dataset applications the data must be stored in space and query efficient data structures on external storage devices. Often the data needs to be changed dynamically. In this chapter we discuss recent advances in the development of provably worstcase efficient external memory dynami ..."
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Cited by 81 (36 self)
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In many massive dataset applications the data must be stored in space and query efficient data structures on external storage devices. Often the data needs to be changed dynamically. In this chapter we discuss recent advances in the development of provably worstcase efficient external memory dynamic data structures. We also briefly discuss some of the most popular external data structures used in practice.
Externalmemory breadthfirst search with sublinear I/O
 IN PROCEEDINGS OF THE 10TH ANNUAL EUROPEAN SYMPOSIUM ON ALGORITHMS
, 2002
"... Breadthfirst search (BFS) is a basic graph exploration technique. We give the first external memory algorithm for sparse undirected graphs with sublinear I/O. The best previous algorithm requires \Theta (n + n+mD\Delta B \Delta logM=B n+mB) I/Os on a graph with n nodes and m edges and a machine w ..."
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Cited by 47 (13 self)
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Breadthfirst search (BFS) is a basic graph exploration technique. We give the first external memory algorithm for sparse undirected graphs with sublinear I/O. The best previous algorithm requires \Theta (n + n+mD\Delta B \Delta logM=B n+mB) I/Os on a graph with n nodes and m edges and a machine with mainmemory of size M, D parallel disks, and block size B. We present two versions of a new algorithm which requires only O i (p 1D\Delta B + p nm) \Delta n+mpD\Delta B \Delta logM=B n+mB
On External Memory MST, SSSP and Multiway Planar Graph Separation (Extended Abstract)
, 2000
"... Recently external memory graph algorithms have received considerable attention because massive graphs arise naturally in many applications involving massive data sets. Even though a large number of I/Oefficient graph algorithms have been developed, a number of fundamental problems still remain ..."
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Cited by 33 (11 self)
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Recently external memory graph algorithms have received considerable attention because massive graphs arise naturally in many applications involving massive data sets. Even though a large number of I/Oefficient graph algorithms have been developed, a number of fundamental problems still remain open. In this paper we develop improved algorithms for the problem of computing a minimum spanning tree of a general graph G = (V; E), as well as new algorithms for the single source shortest paths and the multiway graph separation problems on planar graphs.
On externalmemory MST, SSSP and multiway planar graph separation
 In Proc. 8th Scandinavian Workshop on Algorithmic Theory, volume 1851 of LNCS
, 2000
"... Recently external memory graph algorithms have received considerable attention because massive graphs arise naturally in many applications involving massive data sets. Even though a large number of I/Oefficient graph algorithms have been developed, a number of fundamental problems still remain open ..."
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Cited by 24 (2 self)
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Recently external memory graph algorithms have received considerable attention because massive graphs arise naturally in many applications involving massive data sets. Even though a large number of I/Oefficient graph algorithms have been developed, a number of fundamental problems still remain open. In this paper we develop an improved algorithm for the problem of computing a minimum spanning tree of a general graph, as well as new algorithms for the single source shortest paths and the multiway graph separation problems on planar graphs.
On ExternalMemory Planar Depth First Search
 Journal of Graph Algorithms and Applications
"... Even though a large number of I/Oefficient graph algorithms have been developed, a number of fundamental problems still remain open. For example, no space and I/Oefficient algorithms are known for depthfirst search or breadthfirst search in sparse graphs. In this paper we present two new re ..."
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Cited by 24 (15 self)
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Even though a large number of I/Oefficient graph algorithms have been developed, a number of fundamental problems still remain open. For example, no space and I/Oefficient algorithms are known for depthfirst search or breadthfirst search in sparse graphs. In this paper we present two new results on I/Oefficient depthfirst search in an important class of sparse graphs, namely undirected embedded planar graphs. We develop a new efficient depthfirst search algorithm and show how planar depthfirst search in general can be reduced to planar breadthfirst search. As part of the first result we develop the first I/Oefficient algorithm for finding a simple cycle separator of a biconnected planar graph. Together with other recent reducibility results, the second result provides further evidence that external memory breadthfirst search is among the hardest problems on planar graphs. 1
A computational study of externalmemory BFS algorithms
 In SODA
, 2006
"... Breadth First Search (BFS) traversal is an archetype for many important graph problems. However, computing a BFS level decomposition for massive graphs was considered nonviable so far, because of the large number of I/Os it incurs. This paper presents the first experimental evaluation of recent exte ..."
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Cited by 18 (4 self)
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Breadth First Search (BFS) traversal is an archetype for many important graph problems. However, computing a BFS level decomposition for massive graphs was considered nonviable so far, because of the large number of I/Os it incurs. This paper presents the first experimental evaluation of recent externalmemory BFS algorithms for general graphs. With our STXXL based implementations exploiting pipelining and diskparallelism, we were able to compute the BFS level decomposition of a webcrawl based graph of around 130 million nodes and 1.4 billion edges in less than 4 hours using single disk and 2.3 hours using 4 disks. We demonstrate that some rather simple externalmemory algorithms perform significantly better (minutes as compared to hours) than internalmemory BFS, even if more than half of the input resides internally. 1
Flow Computation on Massive Grid Terrains
 GEOINFORMATICA
, 2001
"... ... In this paper we present efficient algorithms for flow routing on massive terrains, extending our previous work on flow accumulation on massive terrains. We have implemented these algorithms in the Terraflow system, which is the first comprehensive terrain flow software system designed and optim ..."
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Cited by 14 (9 self)
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... In this paper we present efficient algorithms for flow routing on massive terrains, extending our previous work on flow accumulation on massive terrains. We have implemented these algorithms in the Terraflow system, which is the first comprehensive terrain flow software system designed and optimized for massive data. We compare the performance of Terraflow with that of state of the art commercial and opensource GIS systems. On large terrains, Terraflow outperforms existing systems by a factor of 2 to 1000, and is capable of solving problems no system was previously able to solve.
TerraStream: From elevation data to watershed hierarchies
 Proc. ACM Sympos. on Advances in Geographic Information Systems
"... We consider the problem of extracting a river network and a watershed hierarchy from a terrain given as a set of irregularly spaced points. We describe TerraStream, a “pipelined ” solution that consists of four main stages: construction of a digital elevation model (DEM), hydrological conditioning, ..."
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Cited by 11 (7 self)
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We consider the problem of extracting a river network and a watershed hierarchy from a terrain given as a set of irregularly spaced points. We describe TerraStream, a “pipelined ” solution that consists of four main stages: construction of a digital elevation model (DEM), hydrological conditioning, extraction of river networks, and construction of a watershed hierarchy. Our approach has several advantages over existing methods. First, we design and implement the pipeline so each stage is scalable to massive data sets; a single nonscalable stage would create a bottleneck and limit overall scalability. Second, we develop the algorithms in a general framework so that they work for both TIN and grid DEMs. TerraStream is flexible and allows users to choose from various models and parameters, yet our pipeline is designed to reduce (or eliminate) the need for manual intervention between stages. We have implemented TerraStream and present experimental results on real elevation point sets that show that our approach handles massive multigigabyte terrain data sets. For example, we can process a data set containing over 300 million points—over 20GB of raw data—in under 26 hours, where most of the time (76%) is spent in the initial CPUintensive DEM construction stage. 1