Efficient general spatial skyline computation

Lin, Qianlu; Zhang, Ying; Zhang, Wenjie; Lin, Xuemin

doi:10.1007/s11280-012-0185-1

Cited by 9 publications

(9 citation statements)

References 18 publications

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“…12 shows an illustration of window query's minimum and maximum value in one dimension. Assume min and max distance for g, q, and g are (10,15), (20,25), and (30,35). For (15)(16)(17)(18)(19)(20), so that min(w k (g)) and max(w k (g)) are 5 (10 + (-5)) and 20 (same value as min(d k (q))).…”

Section: ) Window Querymentioning

confidence: 99%

Reverse Area Skyline in a Map

Annisa¹,

Zaman²,

Morimoto³

2017

ijacsa

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Abstract-Skyline query retrieves a set of data objects, each of which is not dominated by another object. On the other hand, given a query object q, "reverse" skyline query retrieves a set of points that are "dynamic" skyline of q. If q is a given preference of a user, "dynamic" skyline query retrieves a set of points that are not dominated by another point with respect to q. Intuitively,"reverse" skyline query of q retrieves a set of points that are as preferable as q. Area skyline query is a method for selecting good areas, each of which is near to desirable facilities such as stations, warehouses, promising customers' house, etc. and is far from undesirable facilities such as competitors' shops, noise sources, etc. In this paper, we applied reverse skyline concept to area skyline query and proposed Reverse Area Skyline algorithm. Analogically, given an area g, reverse area skyline query selects areas, each of which are as preferable as g. Assume that a real estate company wants to sell an area. Reverse area skyline query must be useful for such company to consider effective real estate developments so that the area attracts many buyers. Reverse area skyline query can also be used for selecting promising buyers of the area.

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Section: ) Window Querymentioning

confidence: 99%

Reverse Area Skyline in a Map

Annisa¹,

Zaman²,

Morimoto³

2017

ijacsa

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“…[3], [7], [8], [10]. In spatial skyline, distance is the first parameter that needs to be considered.…”

Section: Spatial Skyline Querymentioning

confidence: 99%

Area Skyline Query for Selecting Good Locations in a Map

Annisa

Zaman

Morimoto

2016

Journal of Information Processing

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We present a method for selecting good locations, each of which is close to desirable facilities such as stations, warehouses, promising customers' house, etc. and is far from undesirable facilities such as competitors' shops, noise sources, etc. Skyline query, which selects non-dominated objects, is a well known method for selecting small number of desirable objects. We use the idea of skyline queries to select good locations. However, locations are two dimensional data, while objects in the problem of conventional skyline queries are zero dimensional data. Comparison of two dimensional data is much more complicated than that of zero dimensional data. In this paper, we solve the problem of skyline query for two dimensional data, i.e., areas in a map. Experimental evaluations of the proposed method shows that our approach is able to find reasonable number of desirable skyline areas and can help users to find good locations.

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“…In recent years, skyline query processing [16][17][18][19][20][21] has attracted much of attention due to its suitability for decision-making applications such as top-k spatial preference queries. Lin et al [16] studied the general spatial skyline (GSSKY) problem, which generates a minimal candidate set comprising the optimal solutions for any monotonic distance-based spatial preference query.…”

Section: Skyline Queriesmentioning

confidence: 99%

“…Lin et al [16] studied the general spatial skyline (GSSKY) problem, which generates a minimal candidate set comprising the optimal solutions for any monotonic distance-based spatial preference query. They proposed an efficient progressive algorithm called P-GSSKY, which considerably reduces the number of non-promising objects during computation.…”

Section: Skyline Queriesmentioning

confidence: 99%

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Top-k Spatial Preference Queries in Directed Road Networks

Attique

Cho

Jin

et al. 2016

IJGI

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Top-k spatial preference queries rank objects based on the score of feature objects in their spatial neighborhood. Top-k preference queries are crucial for a wide range of location based services such as hotel browsing and apartment searching. In recent years, a lot of research has been conducted on processing of top-k spatial preference queries in Euclidean space. While few algorithms study top-k preference queries in road networks, they all focus on undirected road networks. In this paper, we investigate the problem of processing the top-k spatial preference queries in directed road networks where each road segment has a particular orientation. Computation of data object scores requires examining the scores of each feature object in its spatial neighborhood. This may cause the computational delay, thus resulting in a high query processing time. In this paper, we address this problem by proposing a pruning and grouping of feature objects to reduce the number of feature objects. Furthermore, we present an efficient algorithm called TOPS that can process top-k spatial preference queries in directed road networks. Experimental results indicate that our algorithm significantly reduces the query processing time compared to period solution for a wide range of problem settings.

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Efficient general spatial skyline computation

Cited by 9 publications

References 18 publications

Reverse Area Skyline in a Map

Reverse Area Skyline in a Map

Area Skyline Query for Selecting Good Locations in a Map

Top-k Spatial Preference Queries in Directed Road Networks

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