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58
The spatial skyline queries
 In VLDB
, 2006
"... In this paper, for the first time, we 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 ..."
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Cited by 75 (7 self)
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In this paper, for the first time, we 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 not dominated by any other point in P considering their derived spatial attributes. The main difference with the regular skyline query is that this spatial domination depends on the location of the query points Q. SSQ has application in several domains such as emergency response and online maps. The main intuition and novelty behind our approaches is that we exploit the geometric properties of the SSQ problem space to avoid the exhaustive examination of all the point pairs in P and Q. Consequently, we reduce the complexity of SSQ search from O(P  2 Q) to
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
Continuous Nearest Neighbor Queries over Sliding Windows
 IEEE Transactions on Knowledge and Data Engineering (TKDE
, 2007
"... Abstract—This paper studies continuous monitoring of nearest neighbor (NN) queries over sliding window streams. According to this model, data points continuously stream in the system, and they are considered valid only while they belong to a sliding window that contains 1) the W most recent arrivals ..."
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Cited by 21 (3 self)
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Abstract—This paper studies continuous monitoring of nearest neighbor (NN) queries over sliding window streams. According to this model, data points continuously stream in the system, and they are considered valid only while they belong to a sliding window that contains 1) the W most recent arrivals (countbased) or 2) the arrivals within a fixed interval W covering the most recent time stamps (timebased). The task of the query processor is to constantly maintain the result of longrunning NN queries among the valid data. We present two processing techniques that apply to both countbased and timebased windows. The first one adapts conceptual partitioning, the best existing method for continuous NN monitoring over update streams, to the sliding window model. The second technique reduces the problem to skyline maintenance in the distancetime space and precomputes the future changes in the NN set. We analyze the performance of both algorithms and extend them to variations of NN search. Finally, we compare their efficiency through a comprehensive experimental evaluation. The skylinebased algorithm achieves lower CPU cost, at the expense of slightly larger space overhead. Index Terms—Locationdependent and sensitive, spatial databases, query processing, nearest neighbors, data streams, sliding windows. 1
BerlinMOD: A Benchmark for Moving Object Databases
, 2007
"... This document presents a method to design scalable and representative moving object data (MOD) and a set of queries for benchmarking spatiotemporal DBMS. Instead of programming a dedicated generator software, we use the existing Secondo DBMS to create benchmark data. The benchmark is based on a sim ..."
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Cited by 21 (5 self)
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This document presents a method to design scalable and representative moving object data (MOD) and a set of queries for benchmarking spatiotemporal DBMS. Instead of programming a dedicated generator software, we use the existing Secondo DBMS to create benchmark data. The benchmark is based on a simulation scenario, where the positions of a sample of vehicles are observed for an arbitrary period of time within the street network of Berlin. We demonstrate the data generator’s extensibility by showing how to achieve more natural movement generation patterns, and how to disturb the vehicles’ positions to create noisy data. As an application and for reference, we also present first benchmarking results for the Secondo DBMS. Such a benchmark is useful in several ways: It provides welldefined data sets and queries for experimental evaluations; it simplifies experimental repeatability; it emphasizes the development of complete systems; it points out weaknesses in existing systems motivating further research. Moreover, the BerlinMOD benchmark allows one to compare different representations of the same moving objects.
Outofcore coherent closed quasiclique mining from large dense graph databases
 ACM TODS
"... Due to the ability of graphs to represent more generic and more complicated relationships among different objects, graph mining has played a significant role in data mining, attracting increasing attention in the data mining community. In addition, frequent coherent subgraphs can provide valuable k ..."
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Cited by 20 (1 self)
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Due to the ability of graphs to represent more generic and more complicated relationships among different objects, graph mining has played a significant role in data mining, attracting increasing attention in the data mining community. In addition, frequent coherent subgraphs can provide valuable knowledge about the underlying internal structure of a graph database, and mining frequently occurring coherent subgraphs from large dense graph databases has witnessed several applications and received considerable attention in the graph mining community recently. In this article, we study how to efficiently mine the complete set of coherent closed quasicliques from large dense graph databases, which is an especially challenging task due to the fact that the downwardclosure property no longer holds. By fully exploring some properties of quasicliques, we propose several novel optimization techniques which can prune the unpromising and redundant subsearch spaces effectively. Meanwhile, we devise an efficient closure checking scheme to facilitate the discovery of closed quasicliques only. Since large databasescannot be held in main memory, we also design an A preliminary conference version of this article entitled “Coherent Closed QuasiClique Discovery
Incremental Evaluation of Visible Nearest Neighbor Queries
"... In many applications involving spatial objects, we are only interested in objects that are directly visible from query points. In this article, we formulate the visible k nearest neighbor (VkNN) query and present incremental algorithms as a solution, with two variants differing in how to prune obj ..."
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Cited by 14 (6 self)
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In many applications involving spatial objects, we are only interested in objects that are directly visible from query points. In this article, we formulate the visible k nearest neighbor (VkNN) query and present incremental algorithms as a solution, with two variants differing in how to prune objects during the search process. One variant applies visibility pruning to only objects, whereas the other variant applies visibility pruning to index nodes as well. Our experimental results show that the latter outperforms the former. We further propose the aggregate VkNN query, which finds the visible k nearest objects to a set of query points based on an aggregate distance function. We also propose two approaches to processing the aggregate VkNN query. One accesses the database via multiple VkNN queries, whereas the other issues an aggregate k nearest neighbor query to retrieve objects from the database and then rerank the results based on the aggregate visible distance metric. With extensive experiments, we show that the latter approach consistently outperforms the former one.
Probabilistic Group Nearest Neighbor Queries in Uncertain Databases
"... Abstract—The importance of query processing over uncertain data has recently arisen due to its wide usage in many realworld applications. In the context of uncertain databases, previous works have studied many query types such as nearest neighbor query, range query, topk query, skyline query, and ..."
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Cited by 13 (0 self)
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Abstract—The importance of query processing over uncertain data has recently arisen due to its wide usage in many realworld applications. In the context of uncertain databases, previous works have studied many query types such as nearest neighbor query, range query, topk query, skyline query, and similarity join. In this paper, we focus on another important query, namely, probabilistic group nearest neighbor (PGNN) query, in the uncertain database, which also has many applications. Specifically, given a set, Q, of query points, a PGNN query retrieves data objects that minimize the aggregate distance (e.g., sum, min, and max) to query set Q. Due to the inherent uncertainty of data objects, previous techniques to answer group nearest neighbor (GNN) query cannot be directly applied to our PGNN problem. Motivated by this, we propose effective pruning methods, namely, spatial pruning and probabilistic pruning, to reduce the PGNN search space, which can be seamlessly integrated into our PGNN query procedure. Extensive experiments have demonstrated the efficiency and effectiveness of our proposed approach, in terms of the wall clock time and the speedup ratio against linear scan. Index Terms—Probabilistic group nearest neighbor queries, uncertain database. 1
Superseding Nearest Neighbor Search on Uncertain Spatial Databases
"... This paper proposes a new problem, called superseding nearest neighbor search, on uncertain spatial databases, where each object is described by a multidimensional probability density function. Given a query point q, an object is a nearest neighbor (NN) candidate if it has a nonzero probability to ..."
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Cited by 12 (0 self)
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This paper proposes a new problem, called superseding nearest neighbor search, on uncertain spatial databases, where each object is described by a multidimensional probability density function. Given a query point q, an object is a nearest neighbor (NN) candidate if it has a nonzero probability to be the NN of q. Given two NN candidates o1 and o2, o1 supersedes o2 if o1 is more likely to be closer to q. An object is a superseding nearest neighbor (SNN) of q, if it supersedes all the other NNcandidates. Sometimes no object is able to supersede every other NN candidate. In this case, we return the SNNcore — the minimum set of NNcandidates each of which supersedes all the NNcandidates outside the SNNcore. Intuitively, the SNNcore contains the best objects, because any object outside the SNNcore is worse than all the objects in the SNNcore. We show that the SNNcore can be efficiently computed by utilizing a conventional multidimensional index, as confirmed by extensive experiments.
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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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 not dominated by any other point in P considering their derived spatial attributes. The main difference with the regular skyline query is that this spatial domination depends on the location of the query points Q. SSQ has application in several domains such as emergency response and online maps. The main intuition and novelty behind our approaches is that we exploit the geometric properties of the SSQ problem space to avoid the exhaustive examination of all the point pairs in P and Q. Consequently, we reduce the complexity of SSQ search from O(P  2 Q)toO(S  2 C+ √ P), where S  and C  are the solution size and the number of vertices of the convex hull of Q, respectively. Considering Euclidean distance, we propose two algorithms, B2S2 and VS2, for static query points and one algorithm, VCS2, for streaming Q whose points change location over time (e.g., are mobile). VCS2 exploits the pattern 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
Retrieving knearest neighboring trajectories by a set of point locations
 In SSTD
, 2011
"... Abstract. The advance of object tracking technologies leads to huge volumes of spatiotemporal data accumulated in the form of location trajectories. Such data bring us new opportunities and challenges in efficient trajectory retrieval. In this paper, we study a new type of query that finds the k Ne ..."
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Cited by 10 (6 self)
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Abstract. The advance of object tracking technologies leads to huge volumes of spatiotemporal data accumulated in the form of location trajectories. Such data bring us new opportunities and challenges in efficient trajectory retrieval. In this paper, we study a new type of query that finds the k Nearest Neighboring Trajectories (kNNT) with the minimum aggregated distance to a set of query points. Such queries, though have a broad range of applications like trip planning and moving object study, cannot be handled by traditional kNN query processing techniques that only find the neighboring points of an object. To facilitate scalable, flexible and effective query execution, we propose a kNN trajectory retrieval algorithm using a candidategenerationandverification strategy. The algorithm utilizes a data structure called global heap to retrieve candidate trajectories near each individual query point. Then, at the verification step, it refines these trajectory candidates by a lowerbound computed based on the global heap. The global heap guarantees the candidate’s completeness (i.e., all the kNNTs are included), and reduces the computational overhead of candidate verification. In addition, we propose a qualifier expectation measure that ranks partialmatching candidate trajectories to accelerate query processing in the cases of nonuniform trajectory distributions or outlier query locations. Extensive experiments on both real and synthetic trajectory datasets demonstrate the feasibility and effectiveness of proposed methods. 1