Parallel Computation of Skyline Queries

Woods, Louis; Alonso, Gustavo; Teubner, Jens

doi:10.1109/fccm.2013.18

Cited by 29 publications

(14 citation statements)

References 11 publications

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“…However, these techniques are based on the assumption that communication between processing units (servers) is very costly compared to local processing, thus making them unsuitable for direct adaption in a multicore context. There has also been other recent work on computing skylines using specialized parallel hardware, e.g., GPU [3] and FPGA [16].…”

Section: Related Workmentioning

confidence: 99%

APSkyline: Improved Skyline Computation for Multicore Architectures

Liknes

Vlachou

Doulkeridis

et al. 2014

Database Systems for Advanced Applications

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Abstract. The trend towards in-memory analytics and CPUs with an increasing number of cores calls for new algorithms that can efficiently utilize the available resources. This need is particularly evident in the case of CPU-intensive query operators. One example of such a query with applicability in data analytics is the skyline query. In this paper, we present APSkyline, a new approach for multicore skyline query processing, which adheres to the partition-execute-merge framework. Contrary to existing research, we focus on the partitioning phase to achieve significant performance gains, an issue largely overlooked in previous work in multicore processing. In particular, APSkyline employs an angle-based partitioning approach, which increases the degree of pruning that can be achieved in the execute phase, thus significantly reducing the number of candidate points that need to be checked in the final merging phase. APSkyline is extremely efficient for hard cases of skyline processing, as in the cases of datasets with large skyline result sets, where it is meaningful to exploit multicore processing.

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Section: Related Workmentioning

confidence: 99%

APSkyline: Improved Skyline Computation for Multicore Architectures

Liknes

Vlachou

Doulkeridis

et al. 2014

Database Systems for Advanced Applications

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“…For example, [14] shows how the dominance operator can be computed in a branch-free manner to make it suitable for GPU and [15] details a partitioning scheme providing further improvements. Meanwhile, [16] presents a highly scalable parallel FPGA technique for computing the skyline; the authors later generalized this idea into a new computational structure called a shifter list [17].…”

Section: Discussionmentioning

confidence: 99%

MEMOCODE 2015 design contest: Continuous skyline computation

Milder

2015

2015 ACM/IEEE International Conference on Formal Methods and Models for Codesign (MEMOCODE)

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The skyline query operation (also called the "maximum vector problem") is used to identify potentially interesting or useful data points in large sets of multi-dimensional data. When the data change over time (through addition and subtraction of points), this is called the "continuous skyline" query. The 2015 MEMOCODE Design Contest problem is to implement a system to efficiently compute the continuous skyline of dynamic data. Contestants were given one month to develop a system to perform the skyline query, aiming to maximize performance or cost-adjusted performance. Teams were encouraged to consider a variety of computational targets including CPUs, FPGAs, and GPGPUs. The two winning teams, which have been invited to contribute papers describing their techniques, combined careful algorithmic and implementation optimizations; both implemented the system on multicore CPUs.

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“…In their evaluation, the lazy locking scheme [20] is shown to be most efficient in comparison to continuous locking or lock-free synchronization. There is also recent work on computing Skylines using specialized parallel hardware, e.g., GPU [21] and FPGA [22]. In contrast to previous works, our approach is based on the parallel traversal of the lattice structure of a Skyline query.…”

Section: Related Workmentioning

confidence: 98%

High Parallel Skyline Computation over Low-Cardinality Domains

Endres

Kießling

2014

Advances in Databases and Information Systems

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Abstract.A Skyline query retrieves all objects in a dataset that are not dominated by other objects according to some given criteria. Although there are a few parallel Skyline algorithms on multicore processors, it is still a challenging task to fully exploit the advantages of such modern hardware architectures for efficient Skyline computation. In this paper we present high-performance parallel Skyline algorithms based on the lattice structure generated by a Skyline query. We compare our methods with the state-of-the-art algorithms for multicore Skyline processing. Experimental results on synthetic and real datasets show that our new algorithms outperform state-of-the-art multicore Skyline techniques for low-cardinality domains. Our algorithms have linear runtime complexity and fully play on modern hardware architectures.

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Parallel Computation of Skyline Queries

Cited by 29 publications

References 11 publications

APSkyline: Improved Skyline Computation for Multicore Architectures

APSkyline: Improved Skyline Computation for Multicore Architectures

MEMOCODE 2015 design contest: Continuous skyline computation

High Parallel Skyline Computation over Low-Cardinality Domains

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