Scalable skyline computation using a balanced pivot selection technique

Lee, Jongwuk; Hwang, Seung-won

doi:10.1016/j.is.2013.05.005

Cited by 62 publications

(77 citation statements)

References 23 publications

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“…Whereas the sort-based algorithms qiuckly detect dominance relationships, the object-based space partitioning methods of Zhang et al [23] and Lee and Hwang [15] detect incomparability quickly. Both OSP [23] and BSkyTree-P [15] recursively select a data point with which to partition the rest of the data and store the skyline points in a data structure that admits faster Phase I processing.…”

Section: Related Workmentioning

confidence: 99%

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Scalable parallelization of skyline computation for multi-core processors

Chester

Sidlauskas

Assent

et al. 2015

2015 IEEE 31st International Conference on Data Engineering

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Abstract-The skyline is an important query operator for multi-criteria decision making. It reduces a dataset to only those points that offer optimal trade-offs of dimensions. In general, it is very expensive to compute. Recently, multicore CPU algorithms have been proposed to accelerate the computation of the skyline. However, they do not sufficiently minimize dominance tests and so are not competitive with state-of-the-art sequential algorithms.In this paper, we introduce a novel multicore skyline algorithm, Hybrid, which processes points in blocks. It maintains a shared, global skyline among all threads, which is used to minimize dominance tests while maintaining high throughput. The algorithm uses an efficiently-updatable data structure over the shared, global skyline, based on point-based partitioning. Also, we release a large benchmark of optimized skyline algorithms, with which we demonstrate on challenging workloads a 100-fold speedup over state-of-the-art multicore algorithms and a 10-fold speedup with 16 cores over state-of-the-art sequential algorithms.

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Section: Related Workmentioning

confidence: 99%

“…Both OSP [23] and BSkyTree-P [15] recursively select a data point with which to partition the rest of the data and store the skyline points in a data structure that admits faster Phase I processing. Lee and Hwang also introduce, BSkyTree-S, which does not use recursion nor the data structure.…”

Section: Related Workmentioning

confidence: 99%

Scalable parallelization of skyline computation for multi-core processors

Chester

Sidlauskas

Assent

et al. 2015

2015 IEEE 31st International Conference on Data Engineering

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“…Lee et al [17] propose an alternative implementation of [22]. Both approaches rely on quadtrees in which each tree node uses a data item to split the underlying space.…”

Section: Elimination Categorymentioning

confidence: 99%

“…Both approaches rely on quadtrees in which each tree node uses a data item to split the underlying space. The algorithm in [17] exploits the idea of selecting the optimal skyline point to partition the data space. This idea is alternative to the OSPSOnSortingFirst algorithm in [22] which adopts the state-of-the-art sorting function [9] to find the partitioning point.…”

Section: Elimination Categorymentioning

confidence: 99%

“…We implement the OSPSOnPartitioningFirst version of this algorithm. This algorithm can be faster than the alternatives such as OSPSOnSortingFirst in [22] and BSkyTree in [17], because it avoids the cost of selecting the point for partitioning and attempts to prune other points as soon as skyline points are found in their dominating partitions. This method also beats LESS [12] and SaLSa [1] under a wide range of settings.…”

Section: Algorithm Efficiencymentioning

confidence: 99%

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Skyline operator on anti-correlated distributions

Shang

2013

Proc. VLDB Endow.

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Finding the skyline in a multi-dimensional space is relevant to a wide range of applications. The skyline operator over a set of d-dimensional points selects the points that are not dominated by any other point on all dimensions. Therefore, it provides a minimal set of candidates for the users to make their personal trade-off among all optimal solutions.The existing algorithms establish both the worst case complexity by discarding distributions and the average case complexity by assuming dimensional independence. However, the data in the real world is more likely to be anti-correlated. The cardinality and complexity analysis on dimensionally independent data is meaningless when dealing with anticorrelated data. Furthermore, the performance of the existing algorithms becomes impractical on anti-correlated data.In this paper, we establish a cardinality model for anticorrelated distributions. We propose an accurate polynomial estimation for the expected value of the skyline cardinality. Because the high skyline cardinality downgrades the performance of most existing algorithms on anti-correlated data, we further develop a determination and elimination framework which extends the well-adopted elimination strategy. It achieves remarkable effectiveness and efficiency. The comprehensive experiments on both real datasets and benchmark synthetic datasets demonstrate that our approach significantly outperforms the state-of-the-art algorithms under a wide range of settings.

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Parallelization of group‐based skyline computation for multi‐core processors

Zhu

et al. 2017

Concurrency and Computation

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Summary Skyline computation is particularly useful in multi‐criteria decision‐making applications. However, it is inadequate to answer queries that need to analyze not only individual points but also groups of points. Compared to the traditional skyline computation, computing group‐based skyline is much more complicated and expensive. This computational challenge promotes us to use modern computing platforms to accelerate the computation. In this paper, we introduce a novel multi‐core algorithm to compute group‐based skyline. We first compute the skyline layers of a data set in parallel, which are a critical intermediate result. In the algorithm, we maintain an efficiently updatable data structure for the shared global skyline layers, which is used to minimize dominance tests and maintain high throughput. Then we design an efficient parallel algorithm to find group‐based skyline based on the skyline layers. Extensive experimental results on real and synthetic data sets show that our algorithms achieve 10‐fold speedup with 16 parallel threads over state‐of‐the‐art sequential algorithms on challenging workloads.

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Scalable skyline computation using a balanced pivot selection technique

Cited by 62 publications

References 23 publications

Scalable parallelization of skyline computation for multi-core processors

Scalable parallelization of skyline computation for multi-core processors

Skyline operator on anti-correlated distributions

Parallelization of group‐based skyline computation for multi‐core processors

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