Results 11  20
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156
Path Oracles for Spatial Networks
, 2009
"... The advent of locationbased services has led to an increased demand for performing operations on spatial networks in real time. The challenge lies in being able to cast operations on spatial networks in terms of relational operators so that they can be performed in the context of a database. A line ..."
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Cited by 26 (8 self)
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The advent of locationbased services has led to an increased demand for performing operations on spatial networks in real time. The challenge lies in being able to cast operations on spatial networks in terms of relational operators so that they can be performed in the context of a database. A linearsized construct termed a path oracle is introduced that compactly encodes the n2 shortest paths between every pair of vertices in a spatial network having n vertices thereby reducing each of the paths to a single tuple in a relational database and enables finding shortest paths by repeated application of a single SQL SELECT operator. The construction of the path oracle is based on the observed coherence between the spatial positions of both source and destination vertices and the shortest paths between them which facilitates the aggregation of source and destination vertices into groups that share common vertices or edges on the shortest paths between them. With the aid of the WellSeparated Pair (WSP) technique, which has been applied to spatial networks using the network distance measure, a path oracle is proposed that takes O(sdn) space, where s is empirically estimated to be around 12 for road networks, but that can retrieve an intermediate link in a shortest path in O(logn) time using a Btree. An additional construct termed the pathdistance oracle of size O(n · max(sd, 1 d ε)) (empirically (n · max(122, 2.5 2 ε))) is proposed that can retrieve an intermediate vertex as well as an εapproximation of the network distances in O(logn) time using a Btree. Experimental results indicate that the proposed oracles are linear in n which means that they are scalable and can enable complicated query processing scenarios on massive spatial network datasets.
PrivacyAware Mobile Services over Road Networks
"... Consider a mobile client who travels over roads and wishes to receive locationbased services (LBS) from untrusted service providers. How might the user obtain such services without exposing her private position information? Meanwhile, how could the privacy protection mechanism incur no disincentive ..."
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Cited by 26 (4 self)
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Consider a mobile client who travels over roads and wishes to receive locationbased services (LBS) from untrusted service providers. How might the user obtain such services without exposing her private position information? Meanwhile, how could the privacy protection mechanism incur no disincentive, e.g., excessive computation or communication cost, for any service provider or mobile user to participate in such a scheme? We detail this problem and present a general model for privacyaware mobile services. A series of key features distinguish our solution from existing ones: a) it adopts the networkconstrained mobility model (instead of the conventional randomwaypoint model) to capture the privacy vulnerability of mobile users; b) it regards the attack resilience (for mobile users) and the queryprocessing cost (for service providers) as two critical measures for designing location privatization solutions, and provides corresponding analytical models; c) it proposes a robust and scalable location anonymization model, XStar, which best leverages the two measures; d) it introduces multifolded optimizations in implementing XStar, which lead to further performance improvement. A comprehensive experimental evaluation is conducted to validate the analytical models and the efficacy of XStar. 1.
Monitoring path nearest neighbor in road networks
 In SIGMOD
, 2009
"... This paper addresses the problem of monitoring the k nearest neighbors to a dynamically changing path in road networks. Given a destination where a user is going to, this new query returns the kNN with respect to the shortest path connecting the destination and the user’s current location, and thus ..."
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Cited by 25 (3 self)
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This paper addresses the problem of monitoring the k nearest neighbors to a dynamically changing path in road networks. Given a destination where a user is going to, this new query returns the kNN with respect to the shortest path connecting the destination and the user’s current location, and thus provides a list of nearest candidates for reference by considering the whole coming journey. We name this query the kPath Nearest Neighbor query (kPNN). As the user is moving and may not always follow the shortest path, the query path keeps changing. The challenge of monitoring the kPNN for an arbitrarily moving user is to dynamically determine the update locations and then refresh the kPNN efficiently. We propose a threephase Bestfirst Network Expansion (BNE) algorithm for monitoring the kPNN and the corresponding shortest path. In the searching phase, the BNE finds the shortest path to the destination, during which a candidate set that guarantees to include the kPNN is generated at the same time. Then in the verification phase, a heuristic algorithm runs for examining candidates’ exact distances to the query path, and it achieves significant reduction in the number of visited nodes. The monitoring phase deals with computing update locations as well as refreshing the kPNN in different user movements. Since determining the network distance is a costly process, an expansion tree and the candidate set are carefully maintained by the BNE algorithm, which can provide efficient update on the shortest path and the kPNN results. Finally, we conduct extensive experiments on real road networks and show that our methods achieve satisfactory performance.
VoRTree: Rtrees with Voronoi Diagrams for Efficient Processing of Spatial Nearest Neighbor Queries ∗
"... A very important class of spatial queries consists of nearestneighbor (NN) query and its variations. Many studies in the past decade utilize Rtrees as their underlying index structures to address NN queries efficiently. The general approach is to use Rtree in two phases. First, Rtree’s hierarchic ..."
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Cited by 25 (2 self)
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A very important class of spatial queries consists of nearestneighbor (NN) query and its variations. Many studies in the past decade utilize Rtrees as their underlying index structures to address NN queries efficiently. The general approach is to use Rtree in two phases. First, Rtree’s hierarchical structure is used to quickly arrive to the neighborhood of the result set. Second, the Rtree nodes intersecting with the local neighborhood (Search Region) of an initial answer are investigated to find all the members of the result set. While Rtrees are very efficient for the first phase, they usually result in the unnecessary investigation of many nodes that none or only a small subset of their including points belongs to the actual result set. On the other hand, several recent studies showed that the
Reverse Nearest Neighbors in Large Graphs
"... Abstract—A reverse nearest neighbor (RNN) query returns the data objects that have a query point as their nearest neighbor (NN). Although such queries have been studied quite extensively in Euclidean spaces, there is no previous work in the context of large graphs. In this paper, we provide a fundam ..."
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Cited by 23 (1 self)
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Abstract—A reverse nearest neighbor (RNN) query returns the data objects that have a query point as their nearest neighbor (NN). Although such queries have been studied quite extensively in Euclidean spaces, there is no previous work in the context of large graphs. In this paper, we provide a fundamental lemma, which can be used to prune the search space while traversing the graph in search for RNN. Based on it, we develop two RNN methods; an eager algorithm that attempts to prune network nodes as soon as they are visited and a lazy technique that prunes the search space when a data point is discovered. We study retrieval of an arbitrary number k of reverse nearest neighbors, investigate the benefits of materialization, cover several query types, and deal with cases where the queries and the data objects reside on nodes or edges of the graph. The proposed techniques are evaluated in various practical scenarios involving spatial maps, computer networks, and the DBLP coauthorship graph. Index Terms — Query processing, spatial databases, graphs and networks. 1
Analysis and Evaluation of V*kNN: An Efficient Algorithm for Moving kNN Queries
"... The moving k nearest neighbor (MkNN) query continuously finds the k nearest neighbors of a moving query point. MkNN queries can be efficiently processed through the use of safe regions. In general, a safe region is a region within which the query point can move without changing the query answer. Th ..."
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Cited by 23 (18 self)
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The moving k nearest neighbor (MkNN) query continuously finds the k nearest neighbors of a moving query point. MkNN queries can be efficiently processed through the use of safe regions. In general, a safe region is a region within which the query point can move without changing the query answer. This paper presents an incremental saferegionbased technique for answering MkNN queries, called the V*Diagram, as well as analysis and evaluation of its associated algorithm, V*kNN. Traditional saferegion approaches compute a safe region based on the data objects but independent of the query location. Our approachexploitstheknowledgeofthequerylocationand the boundary ofthesearchspaceinadditiontothedata objects. Asaresult,V*kNNhasmuchsmaller I/O and computation coststhanexistingmethods.Wefurtherprovidecostmodels to estimate the number of data accesses for V*kNN and a competitivetechnique,RISkNN.TheV*DiagramandV*kNN are also applicable to the domain of spatial networks and we present algorithms to construct a spatialnetwork V*Diagram. Our experimental results show that V*kNN significantlyoutperforms the competitive technique. The results also verify the accuracy of thec ost models.
Fast Nearest Neighbor Search on Road Networks
 In Proc. EDBT
, 2006
"... Abstract. Nearest neighbor (NN) queries have been extended from Euclidean spaces to road networks. Existing approaches are either based on Dijkstralike network expansion or NN/distance precomputation. The former may cause an explosive number of node accesses for sparse datasets because all nodes cl ..."
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Cited by 20 (2 self)
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Abstract. Nearest neighbor (NN) queries have been extended from Euclidean spaces to road networks. Existing approaches are either based on Dijkstralike network expansion or NN/distance precomputation. The former may cause an explosive number of node accesses for sparse datasets because all nodes closer than the NN to the query must be visited. The latter, e.g., the Voronoi Network Nearest Neighbor (V N 3) approach, can handle sparse datasets but is inappropriate for medium and dense datasets due to its high precomputation and storage overhead. In this paper, we propose a new approach that indexes the network topology based on a novel network reduction technique. It simplifies the network by replacing the graph topology with a set of interconnected treebased structures called SPIE’s. An nd index is developed for each SPIE and our new (k)NN search algorithms on an SPIE follow a predetermined tree path to avoid costly network expansion. By mathematical analysis and experimental results, our new approach is shown to be efficient and robust for various network topologies and data distributions. 1
The Islands Approach to Nearest Neighbor Querying in Spatial Networks
, 2006
"... Much research has recently been devoted to the data management foundations of locationbased mobile services. In one important scenario, the service users are constrained to a transportation network. As a result, query processing in spatial road networks is of interest. In this paper, we propose a ..."
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Cited by 19 (2 self)
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Much research has recently been devoted to the data management foundations of locationbased mobile services. In one important scenario, the service users are constrained to a transportation network. As a result, query processing in spatial road networks is of interest. In this paper, we propose a versatile
Probabilistic threshold k nearest neighbor queries over moving objects in symbolic indoor space
 In Proc. EDBT
, 2010
"... The availability of indoor positioning renders it possible to deploy locationbased services in indoor spaces. Many such services will benefit from the efficient support for k nearest neighbor (kNN) queries over large populations of indoor moving objects. However, existing kNN techniques fall short ..."
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Cited by 19 (4 self)
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The availability of indoor positioning renders it possible to deploy locationbased services in indoor spaces. Many such services will benefit from the efficient support for k nearest neighbor (kNN) queries over large populations of indoor moving objects. However, existing kNN techniques fall short in indoor spaces because these differ from Euclidean and spatial network spaces and because of the limited capabilities of indoor positioning technologies. To contend with indoor settings, we propose the new concept of minimal indoor walking distance (MIWD) along with algorithms and data structures for distance computing and storage; and we differentiate the states of indoor moving objects based on a positioning device deployment graph, utilize these states in effective object indexing structures, and capture the uncertainty of object locations. On these foundations, we study the probabilistic threshold kNN (PTkNN) query. Given a query location q and a probability threshold T, this query returns all subsets of k objects that have probability larger than T of containing the kNN query result of q. We propose a combination of three techniques for processing this query. The first uses the MIWD metric to prune objects that are too far away. The second uses fast probability estimates to prune unqualified objects and candidate result subsets. The third uses efficient probability evaluation for computing the final result on the remaining candidate subsets. An empirical study using both synthetic and real data shows that the techniques are efficient.
The optimal sequenced route query
 VLDB Journal
, 2005
"... Several variations of nearest neighbor (NN) query have been investigated by the database community. However, realworld applications often result in the formulation of new variations of the NN problem demanding new solutions. In this paper, we study an unexploited and novel form of NN queries named O ..."
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Cited by 17 (1 self)
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Several variations of nearest neighbor (NN) query have been investigated by the database community. However, realworld applications often result in the formulation of new variations of the NN problem demanding new solutions. In this paper, we study an unexploited and novel form of NN queries named Optimal Sequenced Route (OSR) query in both vector and metric spaces. OSR strives to find a route of minimum length starting from a given source location and passing through a number of typed locations in a specific sequence imposed on the types of the locations. We first transform the OSR problem into a shortest path problem on a large planar graph. We show that a classic shortest path algorithm such as Dijkstra’s is impractical for most realworld scenarios. Therefore, we propose LORD, a light thresholdbased iterative algorithm, that utilizes various thresholds to filter out the locations that cannot be in the optimal route. Then we propose RLORD, an extension of LORD which uses Rtree to examine the threshold values more efficiently. Finally, LORD and RLORD are not applicable in metric spaces, hence we propose another approach that progressively issues NN queries on different point types to construct the optimal route for the OSR query. Our extensive experiments using both realworld and synthetic datasets verify that our algorithms significantly outperform the Dijkstrabased approach in terms of processing time (up to two orders of magnitude) and required workspace (up to 90 % reduction on average). 1.