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10
The Skyline Operator
 IN ICDE
, 2001
"... We propose to extend database systems by a Skyline operation. This operation filters out a set of interesting points from a potentially large set of data points. A point is interesting if it is not dominated by any other point. For example, a hotel might be interesting for somebody traveling to Nass ..."
Abstract

Cited by 385 (3 self)
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We propose to extend database systems by a Skyline operation. This operation filters out a set of interesting points from a potentially large set of data points. A point is interesting if it is not dominated by any other point. For example, a hotel might be interesting for somebody traveling to Nassau if no other hotel is both cheaper and closer to the beach. We show how SQL can be extended to pose Skyline queries, present and evaluate alternative algorithms to implement the Skyline operation, and show how this operation can be combined with other database operations (e.g., join and Top N).
Shooting Stars in the Sky: An Online Algorithm for Skyline Queries
 In VLDB
, 2002
"... Skyline queries ask for a set of interesting points from a potentially large set of data points. If we are traveling, for instance, a restaurant might be interesting if there is no other restaurant which is nearer, cheaper, and has better food. Skyline queries retrieve all such interesting restauran ..."
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Cited by 192 (0 self)
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Skyline queries ask for a set of interesting points from a potentially large set of data points. If we are traveling, for instance, a restaurant might be interesting if there is no other restaurant which is nearer, cheaper, and has better food. Skyline queries retrieve all such interesting restaurants so that the user can choose the most promising one. In this paper, we present a new online algorithm that computes the Skyline. Unlike most existing algorithms that compute the Skyline in a batch, this algorithm returns the first results immediately, produces more and more results continuously, and allows the user to give preferences during the running time of the algorithm so that the user can control what kind of results are produced next (e.g., rather cheap or rather near restaurants).
Stratified computation of skylines with partiallyordered domains, in
 Proc. of the ACM SIGMOD Int'l Conf. on Management of Data
, 2005
"... In this paper, we study the evaluation of skyline queries with partiallyordered attributes. Because such attributes lack a total ordering, traditional indexbased evaluation algorithms (e.g., NN and BBS) that are designed for totallyordered attributes can no longer prune the space as effectively. O ..."
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Cited by 55 (2 self)
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In this paper, we study the evaluation of skyline queries with partiallyordered attributes. Because such attributes lack a total ordering, traditional indexbased evaluation algorithms (e.g., NN and BBS) that are designed for totallyordered attributes can no longer prune the space as effectively. Our solution is to transform each partiallyordered attribute into a twointeger domain that allows us to exploit indexbased algorithms to compute skyline queries on the transformed space. Based on this framework, we propose three novel algorithms: BBS + is a straightforward adaptation of BBS using the framework, and SDC (Stratification by Dominance Classification) and SDC + are optimized to handle false positives and support progressive evaluation. Both SDC and SDC + exploit a dominance relationship to organize the data into strata. While SDC generates its strata at runtime, SDC + partitions the data into strata offline. We also design two dominance classification strategies (MinPC and MaxPC) to further optimize the performance of SDC and SDC +. We implemented the proposed schemes and evaluated their efficiency. Our results show that our proposed techniques outperform existing approaches by a wide margin, with SDC +MinPC giving the best performance in terms of both response time as well as progressiveness. To the best of our knowledge, this is the first paper to address the problem of skyline query evaluation involving partiallyordered attribute domains. 1.
On High Dimensional Skylines
 EDBT 2006
, 2006
"... In many decisionmaking applications, the skyline query is frequently used to find a set of dominating data points (called skyline points) in a multidimensional dataset. In a highdimensional space skyline points no longer offer any interesting insights as there are too many of them. In this paper ..."
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Cited by 33 (4 self)
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In many decisionmaking applications, the skyline query is frequently used to find a set of dominating data points (called skyline points) in a multidimensional dataset. In a highdimensional space skyline points no longer offer any interesting insights as there are too many of them. In this paper, we introduce a novel metric, called skyline frequency that compares and ranks the interestingness of data points based on how often they are returned in the skyline when different number of dimensions (i.e., subspaces) are considered. Intuitively, a point with a high skyline frequency is more interesting as it can be dominated on fewer combinations of the dimensions. Thus, the problem becomes one of finding topk frequent skyline points. But the algorithms thus far proposed for skyline computation typically do not scale well with dimensionality. Moreover, frequent skyline computation requires that skylines be computed for each of an exponential number of subsets of the dimensions. We present efficient approximate algorithms to address these twin difficulties. Our extensive performance study shows that our approximate algorithm can run fast and compute the correct result on large data sets in highdimensional spaces.
Parallel Skyline Computation on Multicore Architectures
"... With the advent of multicore processors, it has become imperative to write parallel programs if one wishes to exploit the next generation of processors. This paper deals with skyline computation as a case study of parallelizing database operations on multicore architectures. We compare two parallel ..."
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Cited by 9 (1 self)
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With the advent of multicore processors, it has become imperative to write parallel programs if one wishes to exploit the next generation of processors. This paper deals with skyline computation as a case study of parallelizing database operations on multicore architectures. We compare two parallel skyline algorithms: a parallel version of the branchandbound algorithm (BBS) and a new parallel algorithm based on skeletal parallel programming. Experimental results show despite its simple design, the new parallel algorithm is comparable to parallel BBS in speed. For sequential skyline computation, the new algorithm far outperforms sequential BBS when the density of skyline tuples is low.
ConstantTime Convexity Problems on Reconfigurable Meshes
 Journal of Parallel and Distributed Computing
, 1995
"... The purpose of this paper is to demonstrate that the versatility of the reconfigurable mesh can be exploited to devise constanttime algorithms for a number of important computational tasks relevant to robotics, computer graphics, image processing, and computer vision. In all our algorithms, we assu ..."
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Cited by 4 (2 self)
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The purpose of this paper is to demonstrate that the versatility of the reconfigurable mesh can be exploited to devise constanttime algorithms for a number of important computational tasks relevant to robotics, computer graphics, image processing, and computer vision. In all our algorithms, we assume that one or two nvertex (convex) polygons are pretiled, one vertex per processor, onto a reconfigurable mesh of size p n \Theta p n. In this setup, we propose constanttime solutions for testing an arbitrary polygon for convexity, solving the point location problem, solving the supporting lines problem, solving the stabbing problem, determining the minimum area/perimeter corner triangle for a convex polygon, determining the kmaximal vertices of a restricted class of convex polygons, constructing the common tangents of two separable convex polygons, deciding whether two convex polygons intersect, answering queries concerning two convex polygons, and computing the smallest distance bet...
On Dominating Your Neighborhood Profitably
"... Recent research on skyline queries has attracted much interest in the database and data mining community. Given a database, an object belongs to the skyline if it cannot be dominated with respect to the given attributes by any other database object. Current methods have only considered socalled min ..."
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Cited by 2 (1 self)
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Recent research on skyline queries has attracted much interest in the database and data mining community. Given a database, an object belongs to the skyline if it cannot be dominated with respect to the given attributes by any other database object. Current methods have only considered socalled min/max attributes like price and quality which a user wants to minimize or maximize. However, objects can also have spatial attributes like x, y coordinates which can be used to represent relevant constraints on the query results. In this paper, we introduce novel skyline query types taking into account not only min/max attributes but also spatial attributes and the relationships between these different attribute types. Such queries support a microeconomic approach to decision making, considering not only the quality but also the cost of solutions. We investigate two alternative approaches for efficient query processing, a symmetrical one based on offtheshelf index structures, and an asymmetrical one based on index structures with special purpose extensions. Our experimental evaluation using a real dataset and various synthetic datasets demonstrates that the new query types are indeed meaningful and the proposed algorithms are efficient and scalable. 1.
Parallel Skyline Queries
"... In this paper, we design and analyze parallel algorithms for skyline queries. The skyline of a multidimensional set consists of the points for which no other point exists that is at least as good along every dimension. As a framework for parallel computation, we use both the MP model proposed in (Ko ..."
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Cited by 2 (0 self)
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In this paper, we design and analyze parallel algorithms for skyline queries. The skyline of a multidimensional set consists of the points for which no other point exists that is at least as good along every dimension. As a framework for parallel computation, we use both the MP model proposed in (Koutris and Suciu, PODS 2011), which requires that the data is perfectly loadbalanced, and a variation of the model in (Afrati and Ullman, EDBT 2010), the GMP model, which demands weaker load balancing constraints. In addition to load balancing, we want to minimize the number of blocking steps, where all processors must wait and synchronize. We propose a 2step algorithm in the MP model for any dimension of the dataset, as well a 1step algorithm for the case of 2 and 3 dimensions. Moreover, we present a 1step algorithm in the GMP model for any number of dimensions.
SkylineSensitive Joins with LRPruning ∗
"... Efficient processing of skyline queries has been an area of growing interest. Most existing techniques assume that the skyline query is applied to a single data table. Unfortunately, this is not true in many applications where, due to the complexity of the schema, the skyline query may involve attri ..."
Abstract
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Efficient processing of skyline queries has been an area of growing interest. Most existing techniques assume that the skyline query is applied to a single data table. Unfortunately, this is not true in many applications where, due to the complexity of the schema, the skyline query may involve attributes belonging to multiple tables. Recently, various hybrid skylinejoin algorithms have been proposed. However, the current proposals suffer from several drawbacks: they often need to scan the input tables exhaustively in order to obtain the set of skylinejoin results; moreover, the pruning techniques employed to eliminate the tuples are largely based on expensive pairwise tupletotuple comparisons. In this paper, we aim to address these shortcomings by proposing two novel skylinejoin algorithms, namely skylinesensitive join (S 2 J) and symmetric skylinesensitive join (S 3 J), to process skyline queries over multiple tables. Our approaches compute the results using a novel layer/region pruning technique (LRpruning) that prunes the join space in blocks as opposed to individual data points, thereby avoiding excessive pairwise pointtopoint dominance checks. Furthermore, the S 3 J algorithm utilizes an early stopping condition in order to successfully compute the skyline results by accessing only a subset of the input tables. We report extensive experimental results that confirm the advantages of the proposed algorithms over the stateoftheart skylinejoin techniques.