Path computation algorithms for advanced traveller information system (ATIS)

Shekhar, Shashi; Kohli, A.; Coyle, Mark

doi:10.1109/icde.1993.344080

Cited by 54 publications

(44 citation statements)

References 12 publications

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“…In road networks, frequently observed aggregate network queries include route evaluation and path computation queries [35], in which an aggregate property is defined as a function of the attributes of junctions and links. In order to derive the aggregate properties, route evaluation queries require retrieval of all junctions and links in a specified route, which is a sequence of junctions ht 1 ; t 2 ; t 3 ; .…”

Section: Query Processingmentioning

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: Query Processingmentioning

confidence: 99%

Clustering spatial networks for aggregate query processing: A hypergraph approach

Demir

Aykanat

Cambazoğlu

2008

Information Systems

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“…A heap is used to keep the frontier nodes on the "wavefront" together with the distance to the source vertex. In large network, the secondary-memory version of Dijkstra's algorithm is used to just keep frontier nodes in memory to minimize the memory requirement [10,21]. The "wavefront" is expanded by deleting the frontier node with minimum distance to the source vertex in the heap and inserting all adjacent vertices of the deleted one.…”

Section: Multi-resolution Disk Storage Schemementioning

confidence: 99%

A multi-resolution surface distance model for k-NN query processing

et al. 2007

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A spatial k-NN query returns k nearest points in a point dataset to a given query point. To measure the distance between two points, most of the literature focuses on the Euclidean distance or the network distance. For many applications, such as wildlife movement, it is necessary to consider the surface distance, which is computed from the shortest path along a terrain surface. In this paper, we investigate the problem of efficient surface k-NN (sk-NN ) query processing. This is an important yet highly challenging problem because the underlying environment data can be very large and the computational cost of finding the shortest path on a surface can be very high. To minimize the amount of surface data to be used and the cost of surface distance computation, a multiresolution surface distance model is proposed in this paper to take advantage of monotonic distance changes when the distances are computed at different resolution levels. Based on this innovative model, sk-NN queries can be processed efficiently by accessing and processing surface data at a justenough resolution level within a just-enough search region. Our extensive performance evaluations using real world datasets confirm the efficiency of our proposed model.

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“…We use Dijkstra's single-source shortest path finding algorithm [24]. Other shortest path finding algorithms [18], however, can be used, and we will explore those in future work.…”

Section: Shortest Path Finding Algorithmsmentioning

confidence: 99%

“…The road distance between two points can be calculated using a shortest path finding algorithm, for which many well established graph-theoretical algorithms are available in the literature [18], [24]. We use Dijkstra's single-source shortest path finding algorithm [24].…”

Section: Shortest Path Finding Algorithmsmentioning

confidence: 99%

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In-Route Nearest Neighbor Queries

Yoo

Shekhar

2005

Geoinformatica

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Nearest neighbor query is one of the most important operations in spatial databases and their application domains, such as location-based services and advanced traveler information systems. This paper addresses the problem of finding the in-route nearest neighbor (IRNN ) for a query object tuple which consists of a given route with a destination and a current location on it. The IRNN is a facility instance via which the detour from the original route on the way to the destination is smallest. This paper addresses four alternative solution methods. Comparisons among them are presented using an experimental framework. Extensive experiments using real road map datasets are conducted to examine the behaviors of the solutions in terms of five parameters affecting the performance. The overall experiments show that our strategy to reduce the expensive path computations to minimize the response time is reasonable. The spatial distance join-based method always shows better performance with fewer path computations compared to the recursive methods. The computation costs for all methods except the precomputed zone-based method increase with increases in the road map size and the query route length but decrease with increases in the facility density. The precomputed zone-based method shows the most efficiency when there are no updates on the road map.

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Path computation algorithms for advanced traveller information system (ATIS)

Cited by 54 publications

References 12 publications

Clustering spatial networks for aggregate query processing: A hypergraph approach

Clustering spatial networks for aggregate query processing: A hypergraph approach

A multi-resolution surface distance model for k-NN query processing

In-Route Nearest Neighbor Queries

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