Efficient query processing on spatial networks

Sankaranarayanan, Jagan; Alborzi, Houman; Samet, Hanan

doi:10.1145/1097064.1097093

Cited by 99 publications

(104 citation statements)

References 40 publications

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“…In our examples, the sorting supported operations that involve proximity measured in terms of as "the crow flies". However, these representations can also be used to support proximity in a graph such as a road network (e.g., (Sankaranarayanan et al, 2005;Samet et al, 2008)). …”

Section: Discussionmentioning

confidence: 99%

Sorting Spatial Data by Spatial Occupancy

Samet

2009

GeoSpatial Visual Analytics

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Abstract. The increasing popularity of web-based mapping services such as Microsoft Virtual Earth and Google Maps/Earth has led to a dramatic increase in awareness of the importance of location as a component of data for the purposes of further processing as a means of enhancing the value of the nonspatial data and of visualization. Both of these purposes inevitably involve searching. The efficiency of searching is dependent on the extent to which the underlying data is sorted. The sorting is encapsulated by the data structure known as an index that is used to represent the spatial data thereby making it more accessible. The traditional role of the indexes is to sort the data, which means that they order the data. However, since generally no ordering exists in dimensions greater than 1 without a transformation of the data to one dimension, the role of the sort process is one of differentiating between the data and what is usually done is to sort the spatial objects with respect to the space that they occupy. The resulting ordering should be implicit rather than explicit so that the data need not be resorted (i.e., the index need not be rebuilt) when the queries change. The indexes are said to order the space and the characteristics of such indexes are explored further.

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Section: Discussionmentioning

confidence: 99%

Sorting Spatial Data by Spatial Occupancy

Samet

2009

GeoSpatial Visual Analytics

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“…The drawback of this approach is that, for n vertices, the amount of storage could be as high as Oðn 3 Þ. The necessary storage can be reduced to Oðn 2 Þ by taking advantage of the fact that the shortest paths from vertex u to all remaining vertices can be decomposed into subsets based on the first edges on the shortest paths to them from u [9], [10], [11]. The cost is a slower process of retrieving the shortest path which makes use of a sequence of point location operations (e.g., [6]).…”

Section: Introductionmentioning

confidence: 99%

“…The cost is a slower process of retrieving the shortest path which makes use of a sequence of point location operations (e.g., [6]). We have shown that the storage necessary for these subsets can be reduced substantially further to Oðn 1:5 Þ [9], [10], [12] by noting the spatial coherence of the subsets and representing them using a shortest path quadtree, which is a variant of the region quadtree, where the blocks are decomposed until all vertices in the block are in the same subset. Note that the use of the quadtree in that context is primarily to take advantage of its dimension-reducing property [13] to decrease the storage requirements.…”

Section: Introductionmentioning

confidence: 99%

“…The techniques that we develop in this paper are based on our inference that given our assumptions on the proximity of the vertices that comprise A and those that comprise B, and the lack of proximity between A and B, that the network distance to the vertices in A from the vertices in B will more or less be similar and can be approximated by a single value (termed the path coherence property [9], [10], [14]). The novelty of our approach is that in the case of the computation of the distance between two vertices, we show how to correlate the extent of this reduction of the space requirements with the approximation error in the value of the distance that is obtained.…”

Section: Introductionmentioning

confidence: 99%

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Query Processing Using Distance Oracles for Spatial Networks

Sankaranarayanan

Samet

2010

IEEE Trans. Knowl. Data Eng.

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Abstract-The popularity of location-based services and the need to do real-time processing on them has led to an interest in performing queries on transportation networks, such as finding shortest paths and finding nearest neighbors. The challenge here is that the efficient execution of spatial operations usually involves the computation of distance along a spatial network instead of "as the crow flies," which is not simple. Techniques are described that enable the determination of the network distance between any pair of points (i.e., vertices) with as little as OðnÞ space rather than having to store the n 2 distances between all pairs. This is done by being willing to expend a bit more time to achieve this goal such as Oðlog nÞ instead of Oð1Þ, as well as by accepting an error " in the accuracy of the distance that is provided. The strategy that is adopted reduces the space requirements and is based on the ability to identify groups of source and destination vertices for which the distance is approximately the same within some ". The reductions are achieved by introducing a construct termed a distance oracle that yields an estimate of the network distance (termed the "-approximate distance) between any two vertices in the spatial network. The distance oracle is obtained by showing how to adapt the well-separated pair technique from computational geometry to spatial networks. Initially, an "-approximate distance oracle of size Oð n " d Þ is used that is capable of retrieving the approximate network distance in Oðlog nÞ time using a B-tree. The retrieval time can be theoretically reduced further to Oð1Þ time by proposing another "-approximate distance oracle of size Oð n log n " d Þ that uses a hash table. Experimental results indicate that the proposed technique is scalable and can be applied to sufficiently large road networks. For example, a 10-percentapproximate oracle (" ¼ 0:1) on a large network yielded an average error of 0.9 percent with 90 percent of the answers having an error of 2 percent or less and an average retrieval time of 68 seconds. The fact that the network distance can be approximated by one value is used to show how a number of spatial queries can be formulated using appropriate SQL constructs and a few built-in primitives. The result is that these operations can be executed on almost any modern database with no modifications, while taking advantage of the existing query optimizers and query processing strategies.

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“…From the database management perspective, efficient LBS support request the integration of several research advances in indexing [13], [17] and query processing techniques [5], [12], [14], [16]. The development of such services has also indicated several open research issues such as the processing of forecasting queries (i.e., determining future positions of moving points [4]), the support of continuous location change in query processing techniques [3], knowledge discovery from data collected via LBS [10], etc.…”

Section: Introductionmentioning

confidence: 99%

Towards the Next Generation of Location-Based Services

Frentzos

Gratsias

Theodoridis

Web and Wireless Geographical Information Systems

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Abstract. Location-based services (LBS) constitute an emerging application domain rapidly introduced in modern life habits. However, given that LBS already count a few years of commercial life, the services provided are rather naïve, not exploiting the current software capabilities and the recent research advances in the fields of spatial and spatio-temporal data management. The goal of this paper is to fill this gap by first presenting the next generation of locationbased services and then, demonstrating their implementation which takes advantage of both modern commercial software and state-of-the-art spatial network and spatio-temporal databases techniques. Novel techniques are also proposed in fields non-thoroughly addressed by the research community, such as the prediction of future position of objects moving on a road network.

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Efficient query processing on spatial networks

Cited by 99 publications

References 40 publications

Sorting Spatial Data by Spatial Occupancy

Sorting Spatial Data by Spatial Occupancy

Query Processing Using Distance Oracles for Spatial Networks

Towards the Next Generation of Location-Based Services

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