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Anglebased space partitioning for efficient parallel skyline computation
 In SIGMOD Conference
, 2008
"... Recently, skyline queries have attracted much attention in the database research community. Space partitioning techniques, such as recursive division of the data space, have been used for skyline query processing in centralized, parallel and distributed settings. Unfortunately, such gridbased par ..."
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Recently, skyline queries have attracted much attention in the database research community. Space partitioning techniques, such as recursive division of the data space, have been used for skyline query processing in centralized, parallel and distributed settings. Unfortunately, such gridbased partitioning is not suitable in the case of a parallel skyline query, where all partitions are examined at the same time, since many data partitions do not contribute to the overall skyline set, resulting in a lot of redundant processing. In this paper we propose a novel anglebased space partitioning scheme using the hyperspherical coordinates of the data points. We demonstrate both formally as well as through an exhaustive set of experiments that this new scheme is very suitable for skyline query processing in a parallel sharenothing architecture. The intuition of our partitioning technique is that the skyline points are equally spread to all partitions. We also show that partitioning the data according to the hyperspherical coordinates manages to increase the average pruning power of points within a partition. Our novel partitioning scheme alleviates most of the problems of traditional grid partitioning techniques, thus managing to reduce the response time and share the computational workload more fairly. As demonstrated by our experimental study, our technique outperforms grid partitioning in all cases, thus becoming an efficient and scalable solution for skyline query processing in parallel environments. 1.
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 14 (2 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.
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 9 (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.
Efficient and Adaptive Distributed Skyline Computation
 PROCEEDINGS OF THE 22ND INTERNATIONAL CONFERENCE ON SCIENTIFIC AND STATISTICAL DATABASE MANAGEMENT (SSDBM)
, 2010
"... Skyline queries have attracted considerable attention over the last few years, mainly due to their ability to return interesting objects without the need for userdefined scoring functions. In this work, we study the problem of distributed skyline computation and propose an adaptive algorithm toward ..."
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Cited by 4 (0 self)
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Skyline queries have attracted considerable attention over the last few years, mainly due to their ability to return interesting objects without the need for userdefined scoring functions. In this work, we study the problem of distributed skyline computation and propose an adaptive algorithm towards controlling the degree of parallelism and the required network traffic. In contrast to stateoftheart methods, our algorithm handles efficiently diverse preferences imposed on attributes. The key idea is to partition the data using a grid scheme and for each query to build onthefly a dependency graph among partitions which can help in effective pruning. Our algorithm operates in two modes: (i) fullparallel mode, where processors are activated simultaneously or (ii) cascading mode, where processors are activated in a cascading manner using propagation of intermediate results, thus reducing network traffic and potentially increasing throughput. Performance evaluation results, based on reallife and synthetic data sets, demonstrate the scalability with respect to the number of processors and database size.
Parallel Skyline Computation on Multicore
"... 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. First we parallelize t ..."
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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. First we parallelize three sequential skyline algorithms, BBS, SFS, and SSkyline, to see if the design principles of sequential skyline computation also extend to parallel skyline computation. Then we develop a new parallel skyline algorithm PSkyline based on the divideandconquer strategy. Experimental results show that all the algorithms successfully utilize multiple cores to achieve a reasonable speedup. In particular, PSkyline achieves a speedup approximately proportional to the number of cores when it needs a parallel computation the most.