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

Zhu, Haoyang; Zhu, Peidong; Li, Xiaoyong; Liu, Qiang; Xun, Peng

doi:10.1002/cpe.4195

Cited by 7 publications

(3 citation statements)

References 31 publications

(91 reference statements)

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“…Paper [42] proposed the skyline algorithms over data stream, but they focus on individual subjects rather than groups. [33][34][35][36][37][38][39][40] discussed different skyline applications in wireless sensor networks, such as continuous reverse skyline, spatial skyline, distributed dynamic skyline and probabilistic skyline query in the wireless sensor networks.…”

Section: Related Workmentioning

confidence: 99%

G-skyline query over data stream in wireless sensor network

Dong

Cui

et al. 2018

Wireless Netw

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There are much data sampled continuously by sensors in the wireless sensor network. Storing and mining these data can find more potential information and provide help for decision making. As an important technology for data mining and multi-criteria decision, skyline computation can identify the interesting single points for user. In order to analyze the groups of points, the group-based skyline is proposed to query all the Pareto Optimal groups which are not g-dominated by other groups with the same number of points. Existing algorithms about g-skyline can just compute static data. However, data stream is very common in many applications, and it is very important to design algorithm go query g-skyline over data stream. In this paper, we propose new algorithms to compute g-skyline over a data stream. We present sharing strategy and then present two efficient algorithms: point-arriving algorithm and point-expiring algorithm. The experimental results on three kinds of synthetic data and a real stock data show that our algorithms perform efficiently over a data stream.

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Section: Related Workmentioning

confidence: 99%

G-skyline query over data stream in wireless sensor network

Dong

Cui

et al. 2018

Wireless Netw

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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%

“…The calculate functions commonly used in these work were some aggregate functions, such as, SUM, MAX, and MIN. H Zhu et al revealed characteristics of existing algorithms of group skyline and proposed a novel multi-core algorithm to compute group skyline [30,31]. Paper [32] defined the group dominance concept based on uncertain data.…”

Section: Related Workmentioning

confidence: 99%

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

Dong

Cui

et al. 2018

Information

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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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Progressive approaches to flexible group skyline queries

Yang

Zhou

et al. 2021

Knowl Inf Syst

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

Cited by 7 publications

References 31 publications

G-skyline query over data stream in wireless sensor network

G-skyline query over data stream in wireless sensor network

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

Progressive approaches to flexible group skyline queries

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