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60
Scalable Network Distance Browsing in Spatial Databases
, 2008
"... An algorithm is presented for finding the k nearest neighbors in a spatial network in a bestfirst manner using network distance. The algorithm is based on precomputing the shortest paths between all possible vertices in the network and then making use of an encoding that takes advantage of the fact ..."
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Cited by 84 (10 self)
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An algorithm is presented for finding the k nearest neighbors in a spatial network in a bestfirst manner using network distance. The algorithm is based on precomputing the shortest paths between all possible vertices in the network and then making use of an encoding that takes advantage of the fact that the shortest paths from vertex u to all of the remaining vertices can be decomposed into subsets based on the first edges on the shortest paths to them from u. Thus, in the worst case, the amount of work depends on the number of objects that are examined and the number of links on the shortest paths to them from q, rather than depending on the number of vertices in the network. The amount of storage required to keep track of the subsets is reduced by taking advantage of their spatial coherence which is captured by the aid of a shortest path quadtree. In particular, experiments on a number of large road networks as
Continuous Nearest Neighbor Monitoring in Road Networks
 PROCEEDINGS 32 ND VLDB CONFERENCE
, 2006
"... Recent research has focused on continuous monitoring of nearest neighbors (NN) in highly dynamic scenarios, where the queries and the data objects move frequently and arbitrarily. All existing methods, however, assume the Euclidean distance metric. In this paper we study kNN monitoring in road netw ..."
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Cited by 54 (2 self)
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Recent research has focused on continuous monitoring of nearest neighbors (NN) in highly dynamic scenarios, where the queries and the data objects move frequently and arbitrarily. All existing methods, however, assume the Euclidean distance metric. In this paper we study kNN monitoring in road networks, where the distance between a query and a data object is determined by the length of the shortest path connecting them. We propose two methods that can handle arbitrary object and query moving patterns, as well as fluctuations of edge weights. The first one maintains the query results by processing only updates that may invalidate the current NN sets. The second method follows the shared execution paradigm to reduce the processing time. In particular, it groups together the queries that fall in the path between two consecutive intersections in the network, and produces their results by monitoring the NN sets of these intersections. We experimentally verify the applicability of the proposed techniques to continuous monitoring of large data and query sets.
The V*Diagram: A QueryDependent Approach to Moving KNN Queries
, 2008
"... The moving k nearest neighbor (MkNN) query finds the k nearest neighbors of a moving query point continuously. The high potential of reducing the query processing cost as well as the large spectrum of associated applications have attracted considerable attention to this query type from the database ..."
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Cited by 35 (8 self)
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The moving k nearest neighbor (MkNN) query finds the k nearest neighbors of a moving query point continuously. The high potential of reducing the query processing cost as well as the large spectrum of associated applications have attracted considerable attention to this query type from the database community. This paper presents an incremental saferegionbased technique for answering MkNN queries, called the V*Diagram. In general, a safe region is a set of points where the query point can move without changing the query answer. Traditional saferegion approaches compute a safe region based on the data objects but independent of the query location. Our approach exploits the current knowledge of the query point and the search space in addition to the data objects. As a result, the V*Diagram has much smaller IO and computation costs than existing methods. The experimental results show that the V*Diagram outperforms the best existing technique by two orders of magnitude.
Distance indexing on road networks
 In PVLDB
, 2006
"... The processing of kNN and continuous kNN queries on spatial network databases (SNDB) has been intensively studied recently. However, there is a lack of systematic study on the computation of network distances, which is the most fundamental difference between a road network and a Euclidean space. S ..."
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Cited by 29 (0 self)
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The processing of kNN and continuous kNN queries on spatial network databases (SNDB) has been intensively studied recently. However, there is a lack of systematic study on the computation of network distances, which is the most fundamental difference between a road network and a Euclidean space. Since the online Dijkstra’s algorithm has been shown to be efficient only for short distances, we propose an efficient index, called distance signature, for distance computation and query processing over long distances. Distance signature discretizes the distances between objects and network nodes into categories and then encodes these categories. To minimize the storage and search costs, we present the optimal category partition, and the encoding and compression algorithms for the signatures, based on a simplified network topology. By mathematical analysis and experimental study, we showed that the signature index is efficient and robust for various data distributions, query workloads, parameter settings and network updates. 1.
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.
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.