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Query Processing in Spatial Network Databases
 In VLDB
, 2003
"... Despite the importance of spatial networks in reallife applications, most of the spatial database literature focuses on Euclidean spaces. In this paper we propose an architecture that integrates network and Euclidean information, capturing pragmatic constraints. Based on this architecture, we ..."
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Cited by 140 (7 self)
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Despite the importance of spatial networks in reallife applications, most of the spatial database literature focuses on Euclidean spaces. In this paper we propose an architecture that integrates network and Euclidean information, capturing pragmatic constraints. Based on this architecture
VoronoiBased K Nearest Neighbor Search for Spatial Network Databases
 In VLDB
, 2004
"... A frequent type of query in spatial networks (e.g., road networks) is to find the K nearest neighbors (KNN) of a given query object. ..."
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Cited by 156 (16 self)
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A frequent type of query in spatial networks (e.g., road networks) is to find the K nearest neighbors (KNN) of a given query object.
Evacuation Planning: A Spatial Network Database Approach
"... Efficient tools are needed to identify routes and schedules to evacuate affected populations to safety in face of natural disasters or terrorist attacks. Challenges arise due to violation of key assumptions (e.g. stationary ranking of alternative routes, Wardrop equilibrium) behind popular shortest ..."
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Cited by 3 (2 self)
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path algorithms (e.g. Dijkstra’s, A*) and microscopic traffic simulators (e.g. DYNASMART). Timeexpanded graphs (TEG) based mathematical programming paradigm does not scale up to large urban scenarios due to excessive duplication of transportation network across timepoints. We present a new approach
Processing spatial skyline queries in both vector spaces and spatial network databases
 TODS
"... In this article, we first introduce the concept of Spatial Skyline Queries (SSQ). Given a set of data points P and a set of query points Q, each data point has a number of derived spatial attributes each of which is the point’s distance to a query point. An SSQ retrieves those points of P which are ..."
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Cited by 12 (1 self)
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of change in Q to avoid unnecessary recomputation of the skyline and hence efficiently perform updates. We also propose two algorithms, SNS2 and VSNS2, that compute the spatial skyline with respect to the network distance in a spatial network database. Our
Continuous K Nearest Neighbor Queries in Spatial Network Databases
"... Continuous K nearest neighbor queries (CKNN) are defined as the nearest points of interest to all the points on a path (e.g., continuously finding the three nearest gas stations to a moving car). The result of this type of query is a set of intervals (or split points) and their corresponding KNNs, ..."
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, such that the KNNs of all objects within each interval are the same. The current studies on CKNN focus on Euclidean spaces. These studies are not applicable to spatial network databases (SNDB) where the distance between two objects is defined as the length of the shortest path between them. In this paper, we
Multiple k Nearest Neighbor Query Processing in Spatial Network Databases
"... Abstract. This paper concerns the efficient processing of multiple k nearest neighbor queries in a roadnetwork setting. The assumed setting covers a range of scenarios such as the one where a large population of mobile service users that are constrained to a road network issue nearestneighbor quer ..."
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Cited by 8 (2 self)
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Abstract. This paper concerns the efficient processing of multiple k nearest neighbor queries in a roadnetwork setting. The assumed setting covers a range of scenarios such as the one where a large population of mobile service users that are constrained to a road network issue nearest
Using Dijkstra’s algorithm to incrementally find the knearest neighbors in spatial network databases
 IN: PROC. ACM SAC
, 2006
"... One of the most important kinds of queries in Spatial Network Databases (SNDB) to support LocationBased Services (LBS) is the kNearest Neighbors (kNN) query. Given a point in a network, e.g. a location of a car on a road network, and a set of points of interests, e.g. hotels, gas stations, etc., ..."
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Cited by 9 (1 self)
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One of the most important kinds of queries in Spatial Network Databases (SNDB) to support LocationBased Services (LBS) is the kNearest Neighbors (kNN) query. Given a point in a network, e.g. a location of a car on a road network, and a set of points of interests, e.g. hotels, gas stations, etc
A densitybased algorithm for discovering clusters in large spatial databases with noise
, 1996
"... Clustering algorithms are attractive for the task of class identification in spatial databases. However, the application to large spatial databases rises the following requirements for clustering algorithms: minimal requirements of domain knowledge to determine the input parameters, discovery of clu ..."
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Cited by 1786 (70 self)
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Clustering algorithms are attractive for the task of class identification in spatial databases. However, the application to large spatial databases rises the following requirements for clustering algorithms: minimal requirements of domain knowledge to determine the input parameters, discovery
Alternative solutions for continuous K nearest neighbor queries in spatial network databases, GeoInformatica 9 (4
, 2005
"... Continuous K nearest neighbor queries (CKNN) are defined as finding the nearest points of interest along an entire path (e.g., finding the three nearest gas stations to a moving car on any point of a prespecified path). The result of this type of query is a set of intervals (or split points) and t ..."
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Cited by 17 (0 self)
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) and their corresponding KNNs, such that the KNNs of all points within each interval are the same. The current studies on CKNN focus on vector spaces where the distance between two objects is a function of their spatial attributes (e.g., Euclidean distance metric). These studies are not applicable to spatial network
Results 1  10
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