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On kskip Shortest Paths
"... Given two vertices s, t in a graph, let P be the shortest path (SP) from s to t, and P ⋆ a subset of the vertices in P. P ⋆ is a kskip shortest path from s to t, if it includes at least a vertex out of every k consecutive vertices in P. In general, P ⋆ succinctly describes P by sampling the vertice ..."
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Given two vertices s, t in a graph, let P be the shortest path (SP) from s to t, and P ⋆ a subset of the vertices in P. P ⋆ is a kskip shortest path from s to t, if it includes at least a vertex out of every k consecutive vertices in P. In general, P ⋆ succinctly describes P by sampling the vertices in P with a rate of at least 1/k. This makes P ⋆ a natural substitute in scenarios where reporting every single vertex of P is unnecessary or even undesired. This paper studies kskip SP computation in the context of spatial network databases (SNDB). Our technique has two properties crucial for realtime query processing in SNDB. First, our solution is able to answer kskip queries significantly faster than finding the original SPs in their entirety. Second, the previous objective is achieved with a structure that occupies less space than storing the underlying road network. The proposed algorithms are the outcome of a careful theoretical analysis that reveals valuable insight into the characteristics of the kskip SP problem. Their efficiency has been confirmed by extensive experiments with real data.
Shortest Path and Distance Queries on Road Networks: An Experimental Evaluation
"... Computing the shortest path between two given locations in a road network is an important problem that finds applications in various map services and commercial navigation products. The stateoftheart solutions for the problem can be divided into two categories: spatialcoherencebased methods and ..."
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Computing the shortest path between two given locations in a road network is an important problem that finds applications in various map services and commercial navigation products. The stateoftheart solutions for the problem can be divided into two categories: spatialcoherencebased methods and verteximportancebased approaches. The two categories of techniques, however, have not been compared systematically under the same experimental framework, as they were developed from two independent lines of research that do not refer to each other. This renders it difficult for a practitioner to decide which technique should be adopted for a specific application. Furthermore, the experimental evaluation of the existing techniques, as presented in previous work, falls short in several aspects. Some methods were tested only on small road networks with up to one hundred thousand vertices; some approaches were evaluated using distance queries (instead of shortest path queries), namely, queries that ask only for the length of the shortest path; a stateoftheart technique was examined based on a faulty implementation that led to incorrect query results. To address the above issues, this paper presents a comprehensive comparison of the most advanced spatialcoherencebased and verteximportancebased approaches. Using a variety of real road networks with up to twenty million vertices, we evaluated each technique in terms of its preprocessing time, space consumption, and query efficiency (for both shortest path and distance queries). Our experimental results reveal the characteristics of different techniques, based on which we provide guidelines on selecting appropriate methods for various scenarios. 1.
Efficient continuous nearest neighbor query in spatial networks using euclidean restriction
 In SSTD
, 2009
"... Abstract. In this paper, we propose an efficient method to answer continuous k nearest neighbor (CkNN) queries in spatial networks. Assuming a moving query object and a set of data objects that make frequent and arbitrary moves on a spatial network with dynamically changing edge weights, CkNN contin ..."
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Abstract. In this paper, we propose an efficient method to answer continuous k nearest neighbor (CkNN) queries in spatial networks. Assuming a moving query object and a set of data objects that make frequent and arbitrary moves on a spatial network with dynamically changing edge weights, CkNN continuously monitors the nearest (in network distance) neighboring objects to the query. Previous CkNN methods are inefficient and, hence, fail to scale in large networks with numerous data objects because: 1) they heavily rely on Dijkstrabased blind expansion for network distance computation that incurs excessively redundant cost particularly in large networks, and 2) they blindly map all object location updates to the network disregarding whether the updates are relevant to the CkNN query result. With our method, termed ERCkNN (short for Euclidian Restriction based CkNN), we utilize ER to address both of these shortcomings. Specifically, with ER we enable 1) guided search (rather than blind expansion) for efficient network distance calculation, and 2) localized mapping (rather than blind mapping) to avoid the intolerable cost of redundant object location mapping. We demonstrate the efficiency of ERCkNN via extensive experimental evaluations with real world datasets consisting of a variety of large spatial networks with numerous moving objects. 1
Efficient Evaluation of kRange Nearest Neighbor Queries in Road Networks
"... Abstract—A kRange Nearest Neighbor (or kRNN for short) query in road networks finds the k nearest neighbors of every point on the road segments within a given query region based on the network distance. The kRNN query is significantly important for locationbased applications in many realistic scen ..."
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Abstract—A kRange Nearest Neighbor (or kRNN for short) query in road networks finds the k nearest neighbors of every point on the road segments within a given query region based on the network distance. The kRNN query is significantly important for locationbased applications in many realistic scenarios. For example, (1) the user’s location is uncertain, i.e., user’s location is modeled by a spatial region, and (2) the user is not willing to reveal her exact location to preserve her privacy, i.e., her location is blurred into a spatial region. However, the existing solutions for kRNN queries simply apply the traditional knearest neighbor query processing algorithm multiple times, which poses a huge redundant searching overhead. To this end, we propose an efficient kRNN query processing algorithm in this paper. Our algorithm (1) employs a shared execution approach to eliminate the redundant searching overhead, and (2) provides a parameter that can be tuned to achieve a tradeoff between the query processing performance and the storage overhead, while guaranteeing the user’s exact knearest neighbors are included in the query answers. The experimental results show that our algorithm always outperforms the existing solution in terms of query response time, and the introduced tuning parameter is an effective way to achieve the tradeoff between the query response time and the storage overhead. I.
Nearest Keyword Search in XML Documents
"... This paper studies the nearest keyword (NK) problem on XML documents. In general, the dataset is a tree where each node is associated ..."
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This paper studies the nearest keyword (NK) problem on XML documents. In general, the dataset is a tree where each node is associated
Online Computation of Fastest Path in TimeDependent Spatial Networks
, 2011
"... The problem of pointtopoint fastest path computation in static spatial networks is extensively studied with many precomputation techniques proposed to speedup the computation. Most of the existing approaches make the simplifying assumption that traveltimes of the network edges are constant. Howe ..."
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The problem of pointtopoint fastest path computation in static spatial networks is extensively studied with many precomputation techniques proposed to speedup the computation. Most of the existing approaches make the simplifying assumption that traveltimes of the network edges are constant. However, with realworld spatial networks the edge traveltimes are timedependent, where the arrivaltime to an edge determines the actual traveltime on the edge. In this paper, we study the online computation of fastest path in timedependent spatial networks and present a technique which speedsup the path computation. We show that our fastest path computation based on a bidirectional timedependent A * search significantly improves the computation time and storage complexity. With extensive experiments using real datasets (including a variety of large spatial networks with real traffic data) we demonstrate the efficacy of our proposed techniques for online fastest path computation.
Heuristic algorithms for routesearch queries over geographical data
 GIS
"... In a geographical route search, given search terms, the goal is to find an effective route that (1) starts at a given location, (2) ends at a given location, and (3) travels via geographical entities that are relevant to the given terms. A route is effective if it does not exceed a given distance li ..."
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In a geographical route search, given search terms, the goal is to find an effective route that (1) starts at a given location, (2) ends at a given location, and (3) travels via geographical entities that are relevant to the given terms. A route is effective if it does not exceed a given distance limit whereas the ranking scores of the visited entities, with respect to the search terms, are maximal. This paper introduces routesearch queries, suggests three semantics for such queries and deals with the problem of efficiently answering queries under the different semantics. Since the problem of answering routesearch queries is a generalization of the traveling salesman problem, it is unlikely to have an efficient solution, i.e., there is no polynomialtime algorithm that solves the problem (unless P=NP). Hence, in this work we consider heuristics for the problem. Methods for effectively computing routes are presented. The methods are compared analytically and experimentally. For these methods, experiments on both synthetic and realworld data illustrate their efficiency and their effectiveness in computing a route that satisfies the constraints of a routesearch query. Categories and Subject Descriptors
Joint topk spatial keyword query processing.
 IEEE TKDE,
, 2012
"... AbstractWeb users and content are increasingly being geopositioned, and increased focus is being given to serving local content in response to web queries. This development calls for spatial keyword queries that take into account both the locations and textual descriptions of content. We study th ..."
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AbstractWeb users and content are increasingly being geopositioned, and increased focus is being given to serving local content in response to web queries. This development calls for spatial keyword queries that take into account both the locations and textual descriptions of content. We study the efficient, joint processing of multiple topk spatial keyword queries. Such joint processing is attractive during high query loads and also occurs when multiple queries are used to obfuscate a user's true query. We propose a novel algorithm and index structure for the joint processing of topk spatial keyword queries. Empirical studies show that the proposed solution is efficient on real datasets. We also offer analytical studies on synthetic datasets to demonstrate the efficiency of the proposed solution.
Shortest Path Computation with No Information Leakage
"... Shortest path computation is one of the most common queries in locationbased services (LBSs). Although particularly useful, such queries raise serious privacy concerns. Exposing to a (potentially untrusted) LBS the client’s position and her destination may reveal personal information, such as socia ..."
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Shortest path computation is one of the most common queries in locationbased services (LBSs). Although particularly useful, such queries raise serious privacy concerns. Exposing to a (potentially untrusted) LBS the client’s position and her destination may reveal personal information, such as social habits, health condition, shopping preferences, lifestyle choices, etc. The only existing method for privacypreserving shortest path computation follows the obfuscation paradigm; it prevents the LBS from inferring the source and destination of the query with a probability higher than a threshold. This implies, however, that the LBS still deduces some information (albeit not exact) about the client’s location and her destination. In this paper we aim at strong privacy, where the adversary learns nothing about the shortest path query. We achieve this via established private information retrieval techniques, which we treat as blackbox building blocks. Experiments on real, largescale road networks assess the practicality of our schemes. 1.
ISLABEL: an IndependentSet based Labeling Scheme for PointtoPoint Distance Querying
"... We study the problem of computing shortest path or distance between two query vertices in a graph, which has numerous important applications. Quite a number of indexes have been proposed to answer such distance queries. However, all of these indexes can only process graphs of size barely up to 1 mil ..."
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We study the problem of computing shortest path or distance between two query vertices in a graph, which has numerous important applications. Quite a number of indexes have been proposed to answer such distance queries. However, all of these indexes can only process graphs of size barely up to 1 million vertices, which is rather small in view of many of the fastgrowing realworld graphs today such as social networks and Web graphs. We propose an efficient index, which is a novel labeling scheme based on the independent set of a graph. We show that our method can handle graphs of size orders of magnitude larger than existing indexes. 1.