Efficiently computing Pareto optimal G-skyline query in wireless sensor network
There are much data transmitted from sensors in wireless sensor network. How to mine vital information from these large amount of data is very important for decision-making. Aiming at mining more interesting information for users, the skyline technology has attracted more attention due to its widesp...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Hindawi - SAGE Publishing
2021-12-01
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| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1177/15501477211060673 |
| _version_ | 1797714628417945600 |
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| author | Leigang Dong Guohua Liu Xiaowei Cui Quan Yu |
| author_facet | Leigang Dong Guohua Liu Xiaowei Cui Quan Yu |
| author_sort | Leigang Dong |
| collection | DOAJ |
| description | There are much data transmitted from sensors in wireless sensor network. How to mine vital information from these large amount of data is very important for decision-making. Aiming at mining more interesting information for users, the skyline technology has attracted more attention due to its widespread use for multi-criteria decision-making. The point which is not dominated by any other points can be called skyline point. The skyline consists of all these points which are candidates for users. However, traditional skyline which consists of individual points is not suitable for combinations. To address this gap, we focus on the group skyline query and propose efficient algorithm to computing the Pareto optimal group-based skyline (G-skyline). We propose multiple query windows to compute key skyline layers, then optimize the method to compute directed skyline graph, finally introduce primary points definition and propose a fast algorithm based on it to compute G-skyline groups directly and efficiently. The experiments on the real-world sensor data set and the synthetic data set show that our algorithm performs more efficiently than the existing algorithms. |
| first_indexed | 2024-03-12T07:54:45Z |
| format | Article |
| id | doaj.art-44bc600a85e948688babf295a7510173 |
| institution | Directory Open Access Journal |
| issn | 1550-1477 |
| language | English |
| last_indexed | 2024-03-12T07:54:45Z |
| publishDate | 2021-12-01 |
| publisher | Hindawi - SAGE Publishing |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj.art-44bc600a85e948688babf295a75101732023-09-02T20:20:35ZengHindawi - SAGE PublishingInternational Journal of Distributed Sensor Networks1550-14772021-12-011710.1177/15501477211060673Efficiently computing Pareto optimal G-skyline query in wireless sensor networkLeigang Dong0Guohua Liu1Xiaowei Cui2Quan Yu3School of Computer Science and Information Technology, Daqing Normal University, Daqing, ChinaSchool of Computer Science and Technology, Donghua University, Shanghai, ChinaSchool of Computer Science, Baicheng Normal University, Baicheng, ChinaAlibaba’s Ant Group, Beijing, ChinaThere are much data transmitted from sensors in wireless sensor network. How to mine vital information from these large amount of data is very important for decision-making. Aiming at mining more interesting information for users, the skyline technology has attracted more attention due to its widespread use for multi-criteria decision-making. The point which is not dominated by any other points can be called skyline point. The skyline consists of all these points which are candidates for users. However, traditional skyline which consists of individual points is not suitable for combinations. To address this gap, we focus on the group skyline query and propose efficient algorithm to computing the Pareto optimal group-based skyline (G-skyline). We propose multiple query windows to compute key skyline layers, then optimize the method to compute directed skyline graph, finally introduce primary points definition and propose a fast algorithm based on it to compute G-skyline groups directly and efficiently. The experiments on the real-world sensor data set and the synthetic data set show that our algorithm performs more efficiently than the existing algorithms.https://doi.org/10.1177/15501477211060673 |
| spellingShingle | Leigang Dong Guohua Liu Xiaowei Cui Quan Yu Efficiently computing Pareto optimal G-skyline query in wireless sensor network International Journal of Distributed Sensor Networks |
| title | Efficiently computing Pareto optimal G-skyline query in wireless sensor network |
| title_full | Efficiently computing Pareto optimal G-skyline query in wireless sensor network |
| title_fullStr | Efficiently computing Pareto optimal G-skyline query in wireless sensor network |
| title_full_unstemmed | Efficiently computing Pareto optimal G-skyline query in wireless sensor network |
| title_short | Efficiently computing Pareto optimal G-skyline query in wireless sensor network |
| title_sort | efficiently computing pareto optimal g skyline query in wireless sensor network |
| url | https://doi.org/10.1177/15501477211060673 |
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