Algorithms for Performing Polygonal Map Overlay and Spatial Join on Massive Data Sets

Becker, Ludger; Giesen, André; Hinrichs, Klaus; Vahrenhold, Jan

doi:10.1007/3-540-48482-5_17

Cited by 17 publications

(8 citation statements)

References 18 publications

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“…A weighted matching (WM) problem is, for a given (edge weighted) graph G, to find a match of G such that the sum of the edge weights of the match is maximal. The WM problem was solved by J. Edmonds [2] and the complexity of his algorithm is O(n 3 ), where n is the number of nodes of G. For any graph, Edmonds's algorithm outputs a maximal match 1 The basic idea behind the match-based algorithm [6] is first to divide the CO graph into sets of disjoint path graphs 2 such that the sum of the edge weights of the longest paths in the path graphs reaches the maximum, and then link these paths using maximal match among the endpoints of the longest paths in the path graphs.…”

Section: Match-based Heuristicmentioning

confidence: 99%

“…At this sum of the edge weights of the match is maximal among all matches of the graph. 2 A path graph G = (V, E) with n nodes is a graph in which all nodes in V can be listed as a sequence v 1 stage, a sequence of nodes of the longest path for each path graph was output. Any order of these sequences can be taken as an AMO, because the produced path graphs are non-joint with each other, and each node of the original CO graph belongs one and only one path graph.…”

Section: Match-based Heuristicmentioning

confidence: 99%

“…To reduce the CPU and I/O costs for spatial join processing, most spatial join processing methods are performed in two steps (i.e., filter-and-refine approach [1]). The first step chooses pairs of spatial objects that are likely to satisfy the join predicate.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Comparison of Heuristics for Scheduling Spatial Clusters to Reduce I/O Cost in Spatial Join Processing

Xiao

2006

2006 International Conference on Machine Learning and Cybernetics

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Section: Match-based Heuristicmentioning

confidence: 99%

Section: Match-based Heuristicmentioning

confidence: 99%

See 1 more Smart Citation

A Comparison of Heuristics for Scheduling Spatial Clusters to Reduce I/O Cost in Spatial Join Processing

Xiao

2006

2006 International Conference on Machine Learning and Cybernetics

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“…The algorithm adapts a conventional filter-refine paradigm from databases (Brinkhoff et al 1994, Becker et al 1999, Esperanca and Samet 1997, increasing algorithm efficiency by spatially and temporally filtering unnecessary data before performing potentially more expensive spatial computation on filtered data.…”

Section: Introductionmentioning

confidence: 99%

Deferred decentralized movement pattern mining for geosensor networks

Laube

Duckham

Palaniswami

2011

International Journal of Geographical Information Science

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This paper presents an algorithm for decentralized (in-network) data mining of the movement pattern flock amongst mobile geosensor nodes. The algorithm DDIG (Deferred Decentralized Information Grazing) allows roaming sensor nodes to 'graze' over time more information than they could access through their spatially limited perception range alone. The algorithm requires an intrinsic temporal deferral for pattern mining, as sensor nodes must be enabled to collect, memorize, exchange, and integrate their own and their neighbors' most current movement history before reasoning about patterns. A first set of experiments with trajectories of simulated agents showed that the algorithm accuracy increases with growing deferral. A second set of experiments with trajectories of actual tracked livestock reveals some of the shortcomings of the conceptual flocking model underlying DDIG in the context of a smart farming application. Finally, the experiments underline the general conclusion that decentralization in spatial computing can result in imperfect, yet useful knowledge. This paper presents an algorithm for decentralized (in-network) data mining of the movement pattern flock amongst mobile geosensor nodes. The algorithm DDIG (Deferred Decentralized Information Grazing) allows roaming sensor nodes to 'graze' over time more information than they could access through their spatially limited perception range alone. The algorithm requires an intrinsic temporal deferral for pattern mining, as sensor nodes must be enabled to collect, memorize, exchange, and integrate their own and their neighbors' most current movement history before reasoning about patterns. A first set of experiments with trajectories of simulated agents showed that the algorithm accuracy increases with growing deferral. A second set of experiments with trajectories of actual tracked livestock reveals some of the shortcomings of the conceptual flocking model underlying DDIG in the context of a smart farming application. Finally, the experiments underline the general conclusion that decentralization in spatial computing can result in imperfect, yet useful knowledge.

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“…This search of object instances is often done on the basis of information that consists of objects stored in relational tables and organized by thematic layers with spatial indexing methods. In these systems, queries are typically answered as cascaded spatial joins [2,26,[29][30][31].…”

Section: Introductionmentioning

confidence: 99%

Defining and Comparing Content Measures of Topological Relations

Godoy

Rodrı́guez

2004

GeoInformatica

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This work defines and compares three content measures that characterize topological relations between rectangular regions in a two-dimensional space. These content measures use simplified views of spatial objects in order to create an efficient mechanism for capturing the topological content of spatial configurations. The content measures are compared based on the correlation between two similarity rankings: (1) a similarity ranking defined in terms of the distance of content-measure values and (2) a similarity ranking defined in terms of the error of the geometric adjustment between pairs of objects. The correlation between similarity rankings is used as indicator of how well these content measures characterize topological relations. Such content measures provide mechanisms for creating efficient methods to describe and access information on the basis of the topological content of spatial configurations.

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Algorithms for Performing Polygonal Map Overlay and Spatial Join on Massive Data Sets

Cited by 17 publications

References 18 publications

A Comparison of Heuristics for Scheduling Spatial Clusters to Reduce I/O Cost in Spatial Join Processing

A Comparison of Heuristics for Scheduling Spatial Clusters to Reduce I/O Cost in Spatial Join Processing

Deferred decentralized movement pattern mining for geosensor networks

Defining and Comparing Content Measures of Topological Relations

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