Group skyline computation

Im, Hyeonseung; Park, Sung Woo

doi:10.1016/j.ins.2011.11.014

Cited by 49 publications

(48 citation statements)

References 11 publications

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“…The most related to our problem are [26][27][28][29][30][31][32][33]. Paper [26] returned the top-k composition skyline, however, this paper did not propose the composition skyline formally.…”

Section: Related Workmentioning

confidence: 99%

“…Paper [26] returned the top-k composition skyline, however, this paper did not propose the composition skyline formally. Im et al, Zhang et al and Chung et al [27][28][29] defined and researched the group skyline query, they calculated the value of the same attribute of k points to form a group, then compared the dominance relation between the groups using the traditional dominance. The calculate functions commonly used in these work were some aggregate functions, such as, SUM, MAX, and MIN.…”

Section: Related Workmentioning

confidence: 99%

“…In fact, which aggregate function should be used in practical application is difficult to select, so paper [1] proposed the Pareto optimal groups and group-base skyline, which can return all the Pareto optimal solutions. Paper [33] proposed an efficient skyline group algorithm based on the algorithms in paper [27,28] in a data stream, but it was not suitable for the Pareto Optimal groups.…”

Section: Related Workmentioning

confidence: 99%

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Finding Group-Based Skyline over a Data Stream in the Sensor Network

Dong

Cui

et al. 2018

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Abstract:Along with the application of the sensor network, there will be large amount of dynamic data coming from sensors. How to dig the useful information from such data is significant. Skyline query is aiming to identify the interesting points from a large dataset. The group-based skyline query is to find the outstanding Pareto Optimal groups which cannot be g-dominated by any other groups with the group same size. However, the existing algorithms of group-based skyline (G-Skyline) focus on the static data set, how to conduct advanced research on data stream remains an open problem at large. In this paper, we propose the group-based skyline query over the data stream. In order to compute G-Skyline efficiently, we present a sharing strategy, and based on which we propose two algorithms to efficiently compute the G-Skyline over the data stream: the point-arriving algorithm and the point-expiring algorithm. In our experiments, three synthetic data sets are used to test our algorithms; the experiments results show that our algorithms perform efficiently over a data stream.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Finding Group-Based Skyline over a Data Stream in the Sensor Network

Dong

Cui

et al. 2018

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“…The functions map sets to scalar values. The space of sets may consist of all kelement subsets of a given set of tuples [28,35,52], or all the tuple groups in a set of tuples [2]. Now a set T dominates another set S if the profile of T Pareto-dominates the profile of S. This approach to set dominance can be generalized to arbitrary dominance relations [52].…”

Section: Dominance In Other Spacesmentioning

confidence: 99%

Skyline queries, front and back

2013

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Skyline queries are a popular way to obtain preferred answers from the database by providing only the orderings of attribute values. The result of a skyline query consists of those input tuples for which there is no input tuple having better or equal values in all the attributes and a better value in at least one attribute. In this article, we summarize the basic notions and properties of skyline queries, and discuss their extensions and generalizations. In particular, we consider skyline algorithms and skyline cardinality issues.

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“…Although skyline computation is particularly useful in multi‐criteria decision‐making applications, it is inadequate to answer queries that need to analyze not only individual points but also their combinations . Specifically, in many real‐world applications, we need to find groups of points that are not dominated by other groups of equal size.…”

Section: Introductionmentioning

confidence: 99%

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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Group skyline computation

Cited by 49 publications

References 11 publications

Finding Group-Based Skyline over a Data Stream in the Sensor Network

Finding Group-Based Skyline over a Data Stream in the Sensor Network

Skyline queries, front and back

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

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