Results 1  10
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19
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 90 (6 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, we develop a Euclidean restriction and a network expansion framework that take advantage of location and connectivity to efficiently prune the search space. These frameworks are successfully applied to the most popular spatial queries, namely nearest neighbors, range search, closest pairs and edistance joins, in the context of spatial network databases.
Dijkstra's Algorithm OnLine: An Empirical Case Study from Public Railroad Transport
 JOURNAL OF EXPERIMENTAL ALGORITHMICS
, 2000
"... ..."
Geometric SpeedUp Techniques for Finding Shortest Paths in Large Sparse Graphs
, 2003
"... In this paper, we consider Dijkstra's algorithm for the single source single target shortest paths problem in large sparse graphs. The goal is to reduce the response time for online queries by using precomputed information. For the result of the preprocessing, we admit at most linear space. We as ..."
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Cited by 53 (14 self)
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In this paper, we consider Dijkstra's algorithm for the single source single target shortest paths problem in large sparse graphs. The goal is to reduce the response time for online queries by using precomputed information. For the result of the preprocessing, we admit at most linear space. We assume that a layout of the graph is given. From this layout, in the preprocessing, we determine for each edge a geometric object containing all nodes that can be reached on a shortest path starting with that edge. Based on these geometric objects, the search space for online computation can be reduced significantly. We present an extensive experimental study comparing the impact of different types of objects. The test data we use are traffic networks, the typical field of application for this scenario.
Using Multilevel Graphs for Timetable Information in Railway Systems
 IN PROCEEDINGS 4TH WORKSHOP ON ALGORITHM ENGINEERING AND EXPERIMENTS (ALENEX 2002), VOLUME 2409 OF SPRINGER LNCS
, 2002
"... In many fields of application shortest path finding problems in very large graphs arise. Scenarios where large numbers ofonW##O queries for shortest paths have to be processedin realtime appear for examplein tra#cinc5###HF5 systems.In such systems, the techn5Ww# con sidered to speed up the shortes ..."
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Cited by 26 (12 self)
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In many fields of application shortest path finding problems in very large graphs arise. Scenarios where large numbers ofonW##O queries for shortest paths have to be processedin realtime appear for examplein tra#cinc5###HF5 systems.In such systems, the techn5Ww# con sidered to speed up the shortest pathcomputation are usually basedon precomputed incomputed5 On approach proposedoften in thiscon text is a spacereduction where precomputed shortest paths are replaced by sin## edges with weight equal to thelenOq of the corresponres shortest path.In this paper, we give a first systematic experimen tal study of such a spacereduction approach. Wein troduce theconOkW of multilevel graph decomposition Foron specificapplication scenica from the field of timetable information in public tranc ort, we perform a detailed anai ysisan experimen tal evaluation of shortest path computation based on multilevel graph decomposition.
Finding timedependent shortest paths over large graphs
 In Proc. EDBT
, 2008
"... The spatial and temporal databases have been studied widely and intensively over years. In this paper, we study how to answer queries of finding the best departure time that minimizes the total travel time from a place to another, over a road network, where the traffic conditions dynamically change ..."
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Cited by 20 (0 self)
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The spatial and temporal databases have been studied widely and intensively over years. In this paper, we study how to answer queries of finding the best departure time that minimizes the total travel time from a place to another, over a road network, where the traffic conditions dynamically change from time to time. We study a generalized form of this problem, called the timedependent shortestpath problem. A timedependent graph GT is a graph that has an edgedelay function, wi,j(t), associated with each edge (vi, vj), to be stored in a database. The edgedelay function wi,j(t) specifies how much time it takes to travel from node vi to node vj, if it departs from vi at time t. A userspecified query is to ask the minimumtraveltime path, from a source node, vs, to a destination node, ve, over the timedependent graph, GT, with the best departure time to be selected from a time interval T. We denote this user query as LTT(vs, ve, T) over GT. The challenge of this problem is the added complexity due to the time dependency in the timedependent graph. That is, edge delays are not constants, and can vary from time to time. In this paper, we propose a novel algorithm to find the minimumtraveltime path with the best departure time for a LTT(vs, ve, T) query over a large graph GT. Our approach outperforms existing algorithms in terms of both time complexity in theory and efficiency in practice. We will discuss the design of our algorithm, together with its correctness and complexity. We conducted extensive experimental studies over large graphs and will report our findings. 1.
Adaptive fastest path computation on a road network: A traffic mining approach
 In Proc. 2007 Int. Conf. on Very Large Data Bases (VLDB’07
, 2007
"... Efficient fastest path computation in the presence of varying speed conditions on a large scale road network is an essential problem in modern navigation systems. Factors affecting road speed, such as weather, time of day, and vehicle type, need to be considered in order to select fast routes that m ..."
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Cited by 19 (2 self)
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Efficient fastest path computation in the presence of varying speed conditions on a large scale road network is an essential problem in modern navigation systems. Factors affecting road speed, such as weather, time of day, and vehicle type, need to be considered in order to select fast routes that match current driving conditions. Most existing systems compute fastest paths based on road Euclidean distance and a small set of predefined road speeds. However, “History is often the best teacher”. Historical traffic data or driving patterns are often more useful than the simple Euclidean distancebased computation because people must have good reasons to choose these routes, e.g., they may want to avoid those that pass through high crime areas at night or that likely encounter accidents, road construction, or traffic jams. In this paper, we present an adaptive fastest path algorithm capable of efficiently accounting for important driving and speed patterns mined from a large set of traffic data. The algorithm is based on the following observations: (1) The hierarchy of roads can be used to partition the road network into areas, and different path precomputation strategies can be used at the area level, (2) we can limit our route search strategy to edges and path segments that are actually frequently traveled in the data, and (3) drivers usually traverse the road network through the largest roads available given the distance of the trip, except if there are small roads with a significant speed advantage over the large ones. Through an extensive experimental evaluation on real road networks we show that our algorithm provides desirable (short and wellsupported) routes, and that it is significantly faster than competing methods.
Finding fastest paths on a road network with speed patterns
 In Proc. Int. Conf. on Data Engineering (ICDE’06
, 2006
"... This paper proposes and solves the TimeInterval All Fastest Path (allFP) query. Given a userdefined leaving or arrival time interval I, a source node s and an end node e, allFP asks for a set of all fastest paths from s to e, one for each subinterval of I. Note that the query algorithm should fin ..."
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Cited by 18 (0 self)
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This paper proposes and solves the TimeInterval All Fastest Path (allFP) query. Given a userdefined leaving or arrival time interval I, a source node s and an end node e, allFP asks for a set of all fastest paths from s to e, one for each subinterval of I. Note that the query algorithm should find a partitioning of I into subintervals. Existing methods can only be used to solve a very special case of the problem, when the leaving time is a single time instant. A straightforward solution to the allFP query is to run existing methods many times, once for every time instant in I. This paper proposes a solution based on novel extensions to the A * algorithm. Instead of expanding the network many times, we expand once. The travel time on a path is kept as a function of leaving time. Methods to combine traveltime functions are provided to expand a path. A novel lowerbound estimator for travel time is proposed. Performance results reveal that our method is more efficient and more accurate than the discretetime approach. 1
Highperformance multilevel graphs
 IN: 9TH DIMACS IMPLEMENTATION CHALLENGE
, 2006
"... Shortestpath computation is a frequent task in practice. Owing to evergrowing realworld graphs, there is a constant need for faster algorithms. In the course of time, a large number of techniques to heuristically speed up Dijkstra’s shortestpath algorithm have been devised. This work reviews the ..."
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Cited by 15 (4 self)
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Shortestpath computation is a frequent task in practice. Owing to evergrowing realworld graphs, there is a constant need for faster algorithms. In the course of time, a large number of techniques to heuristically speed up Dijkstra’s shortestpath algorithm have been devised. This work reviews the multilevel technique to answer shortestpath queries exactly [SWZ02, HSW06], which makes use of a hierarchical decomposition of the input graph and precomputation of supplementary information. We develop this preprocessing to the maximum and introduce several ideas to enhance this approach considerably, by reorganizing the precomputed data in partial graphs and optimizing them individually. To answer a given query, certain partial graphs are combined to a search graph, which can be explored by a simple and fast procedure. Experiments confirm query times of less than 200 µs for a road graph with over 15 million vertices.
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 13 (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
Statistical Density Prediction in Traffic Networks
, 2008
"... Recently, modern tracking methods started to allow capturing the position of massive numbers of moving objects. Given this information, it is possible to analyze and predict the traffic density in a network which offers valuable information for traffic control, congestion prediction and prevention. ..."
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Cited by 7 (2 self)
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Recently, modern tracking methods started to allow capturing the position of massive numbers of moving objects. Given this information, it is possible to analyze and predict the traffic density in a network which offers valuable information for traffic control, congestion prediction and prevention. In this paper, we propose a novel statistical approach to predict the density on any edge of such a network at some time in the future. Our method is based on shorttime observations of the traffic history. Therefore, knowing the destination of each traveling individual is not required. Instead, we assume that the individuals will act rationally and choose the shortest path from their starting points to their destinations. Based on this assumption, we introduce a statistical approach to describe the likelihood of any given individual in the network to be located at a certain position at a certain time. Since determining this likelihood is quite expensive when done in a straightforward way, we propose an efficient method to speed up the prediction which is based on a suffixtree. In our experiments, we show the capability of our approach to make useful predictions about the traffic density and illustrate the efficiency of our new algorithm when calculating these predictions.