Spatial Skyline Queries: An Efficient Geometric Algorithm

Son, Wanbin; Lee, Mu-Woong; Ahn, Hee-Kap; Hwang, Seung-won

doi:10.1007/978-3-642-02982-0_17

Cited by 30 publications

(14 citation statements)

References 22 publications

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“…a set of query points if x is closer to every query point than y. Efficient algorithms [30,33] have been developed to compute spatial skyline. Nevertheless, the problem studied in this paper is inherently different, since the work in [30,33] focuses on the case where each object has only one instance.…”

Section: D2 Nn Candidate Searchmentioning

confidence: 99%

Optimal Spatial Dominance

Wang

Zhang

et al. 2015

Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data

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In many domains such as computational geometry and database management, an object may be described by multiple instances (points). Then the distance (or similarity) between two objects is captured by the pair-wise distances among their instances. In the past, numerous nearest neighbor (NN) functions have been proposed to define the distance between objects with multiple instances and to identify the NN object. Nevertheless, considering that a user may not have a specific NN function in mind, it is desirable to provide her with a set of NN candidates. Ideally, the set of NN candidates must include every object that is NN for at least one of the NN functions and must exclude every nonpromising object. However, no one has studied the problem of NN candidates computation from this perspective. Although some of the existing works aim at returning a set of candidate objects, they do not focus on the NN functions while computing the candidate objects. As a result, they either fail to include an NN object w.r.t. some NN functions or include a large number of unnecessary objects that have no potential to be the NN regardless of the NN functions.Motivated by this, we classify the existing NN functions for objects with multiple instances into three families by characterizing their key features. Then, we advocate three spatial dominance operators to compute NN candidates where each operator is optimal w.r.t. different coverage of NN functions. Efficient algorithms are proposed for the dominance check and corresponding NN candidates computation. Extensive empirical study on real and synthetic datasets shows that our proposed operators can significantly reduce the number of NN candidates. The comprehensive performance evaluation demonstrates the efficiency of our computation techniques.

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Section: D2 Nn Candidate Searchmentioning

confidence: 99%

Optimal Spatial Dominance

Wang

Zhang

et al. 2015

Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data

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“…Given a set of data points P and a set of query points Q in a ddimensional space, a spatial skyline query retrieves those points of P which are not dominated by any other point in P considering a set of derived spatial attributes, which may consider both spatial properties with respect to objects in Q (like distance [32][33][34] and direction [12]) and non-spatial attributes of P. Alternative skyline definitions have also been provided in the context of road networks [15,35].…”

Section: Related Workmentioning

confidence: 99%

Topological operators: a relaxed query processing approach

2011

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Relaxation and approximation techniques have been proposed as approaches for improving the quality of query results, in terms of completeness and accuracy, in environments where the user may not be able to specify the query in a complete and exact way, since data are quite heterogeneous or she may not know all the characteristics of data at hand. This problem, mainly addressed for relational and XML data, is nowadays quite relevant also for geo-spatial data, due to their increasing usage in highly critical decisional processes. Among geo-spatial queries, those based on spatial and more precisely topological relations are currently used in an increasing number of applications. As far as we know, no approach has been proposed so far for relaxing queries based on topological predicates when they return an empty or insufficient answer, in order to improve result quality and user satisfaction. In this paper, we consider this problem and we present a general relaxation strategy for, possibly multi-domain, topological selection and join queries. Two specific semantics are also provided: the first applies the minimum amount of relaxation in order to get an acceptable answer; the second relaxes the given query of a certain fixed amount, depending on the considered topological predicate. Indexbased processing algorithms, for efficiently executing relaxed queries based on the proposed semantics, are also presented and a specific topological similarity function, to be used for relaxation purposes, is proposed. Experimental results show that the overhead given by query relaxation is acceptable.

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“…Pei et al [23] show that NBA players may be ranked using skyline against their game-by-game statistics, where each player is an uncertain object and the game-by-game statistics of each player are regarded as the instances with the same probability to occur regarding each player (uncertain object). As demonstrated in [30], in many applications, the distances of an object from different facilities are important for the decision making. For example, in order to choose good observation points for forest fire management, we need to consider their distances to hydrology, roadways and fire points [37].…”

Section: Introductionmentioning

confidence: 99%