Towards a framework for high-performance geocomputation: handling vector-topology within a distributed service environment

Dowers, Steve; Gittings, Bruce; Mineter, Michael J.

doi:10.1016/s0198-9715(00)00011-9

Cited by 20 publications

(12 citation statements)

References 12 publications

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“…These fundamental solutions can be the theoretical approaches, such as the grid-based representation of a spatial computational domain [27,30,38], the framework for multilayered libraries between the computing resources and the serial geographic algorithms [39], and the research on the distributed geographic information processing (DGIP) which provides a guiding methodology and principles for implementing geospatial middleware [40][41][42], or the general libraries for processing a particular category of applications, such as the pRPL (parallel raster processing programming library) [43], the PaRGO (parallel raster-based geocomputation operators) [44], etc. In summary, the implementations of these fundamental solutions are mostly based on the powerful computing technologies, including grid computing, cloud computing, and parallel computing.…”

Section: Computationally Intensive Geocomputationmentioning

confidence: 99%

“…Considerable work has been undertaken to design parallel algorithms for processing raster data, while fewer examples are found involving vector data [39]. The examples from the existing approaches are primarily based on the raster data and the spatial point set.…”

Section: Research Problems In Parallelizing Spatial Operationsmentioning

confidence: 99%

“…Most of the existing GIS packages, whether commercial or open source, are realized in a sequential mode. The significant costs of parallel re-implementation of the existed algorithm packages impede the full utilization of the parallel technologies [39]. Obviously, re-writing all the modules is inefficient and unfeasible.…”

Section: Research Problems In Parallelizing Spatial Operationsmentioning

confidence: 99%

See 2 more Smart Citations

Graph-Based Divide and Conquer Method for Parallelizing Spatial Operations on Vector Data

Kang

Lin

2014

Remote Sensing

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Abstract:In computer science, dependence analysis determines whether or not it is safe to parallelize statements in programs. In dealing with the data-intensive and computationally intensive spatial operations in processing massive volumes of geometric features, this dependence can be well utilized for exploiting the parallelism. In this paper, we propose a graph-based divide and conquer method for parallelizing spatial operations (GDCMPSO) on vector data. It can represent spatial data dependences in spatial operations through representing the vector features as graph vertices, and their computational dependences as graph edges. By this way, spatial operations can be parallelized in three steps: partitioning the graph into graph components with inter-component edges firstly, simultaneously processing multiple subtasks indicated by the graph components secondly and finally handling remainder tasks denoted by the inter-component edges. To demonstrate how it works, buffer operation and intersection operation under this paradigm are conducted. In a 12-core environment, the two spatial operations both gain obvious performance improvements, and the speedups are more than eight. The testing results suggest that GDCMPSO contributes to a method for parallelizing spatial operations and can greatly improve the computing efficiency on multi-core architectures.

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Section: Computationally Intensive Geocomputationmentioning

confidence: 99%

Section: Research Problems In Parallelizing Spatial Operationsmentioning

confidence: 99%

Section: Research Problems In Parallelizing Spatial Operationsmentioning

confidence: 99%

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Graph-Based Divide and Conquer Method for Parallelizing Spatial Operations on Vector Data

Kang

Lin

2014

Remote Sensing

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“…Dowers [6] mentioned the difficulty of providing parallel computing in commercial GIS software, since a GIS software manages different data formats, models, algorithms, etc. The authors subsequently exposed that the adoption of standard interfaces [7] by the different GIS solutions unifies data format and available operations and enables the creation of a general model for a parallel GIS implementation.…”

Section: Introductionmentioning

confidence: 99%

Performance enhancement of a GIS-based facility location problem using desktop grid infrastructure

et al. 2013

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Alfonso Laguna, CD.; Hernández García, V. (2013). Performance enhancement of a GIS-based facility location problem using desktop grid infrastructure. Earth Science Informatics. 6(4):199-207. doi:10.1007/s12145-013-0119-1.Performance enhancement of a GIS-based facility location problem using desktop grid infrastructure Andrés García-García (a) , Carolina Perpiñá consisting of a set of the experiments on the case study, concludes that the desktop grid infrastructure is able to use a commercial GIS system to solve the spatial problem achieving high speedup and computational resource utilization. Particularly, the results of the experiments showed an increase in speedup of fourteen times using sixteen computers and a computational efficiency greater than 87% compared with the sequential procedure.

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“…This database has the advantages of high performance, high reliability and good scalability, which enable it to meet requirements of various thick-data applications. However, many challenges emerged when applying the parallel database theory to construct the spatial databases [2][3][4] . In this study, we address the data partitioning problem in parallel spatial databases and present an improved Hilbert curve and a new spatial data partitioning method based on it.…”

Section: Introductionmentioning

confidence: 99%

An improved Hilbert curve for parallel spatial data partitioning

Meng

Zhao²,

Lin³

2007

Geo-spatial Information Science

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A novel Hilbert-curve is introduced for parallel spatial data partitioning, with consideration of the huge-amount property of spatial information and the variable-length characteristic of vector data items. Based on the improved Hilbert curve, the algorithm can be designed to achieve almost-uniform spatial data partitioning among multiple disks in parallel spatial databases. Thus, the phenomenon of data imbalance can be significantly avoided and search and query efficiency can be enhanced.

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Towards a framework for high-performance geocomputation: handling vector-topology within a distributed service environment

Cited by 20 publications

References 12 publications

Graph-Based Divide and Conquer Method for Parallelizing Spatial Operations on Vector Data

Graph-Based Divide and Conquer Method for Parallelizing Spatial Operations on Vector Data

Performance enhancement of a GIS-based facility location problem using desktop grid infrastructure

An improved Hilbert curve for parallel spatial data partitioning

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