道路网环境下K-支配空间Skyline查询方法

李松; 窦雅男; 郝晓红; 张丽平; 郝忠孝

doi:10.7544/issn1000-1239.2020.20190026

道路网环境下K-支配空间Skyline查询方法

¹(哈尔滨理工大学计算机科学与技术学院哈尔滨 150080)
²(哈尔滨工业大学计算机科学与技术学院哈尔滨 150001) (lisongbeifen@163.com)

基金项目: 国家自然科学基金项目(61872105)；黑龙江省留学归国人员科学基金项目(LC2018030)；黑龙江省教育厅科学技术研究项目(12531z004)

详细信息

中图分类号: TP311.13
计量
- 文章访问数: 990
- HTML全文浏览量: 3
- PDF下载量: 217
出版历程
- 发布日期: 2019-12-31

The Method of the K-Dominant Space Skyline Query in Road Network

¹(College of Computer Science and Technology, Harbin University of Science and Technology, Harbin 150080)
²(College of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001)

Funds: This work was supported by the National Natural Science Foundation of China (61872105), the Scientific Research Foundation for Returned Scholars Abroad of Heilongjiang Province of China (LC2018030), and the Science and Technology Research Project of Heilongjiang Provincial Education Department (12531z004).

摘要

摘要: 为了弥补已有的研究成果无法直接处理道路网环境下K-支配空间Skyline查询问题的不足，提出了基于网络Voronoi图的道路网环境下K-支配空间Skyline查询方法.该方法将K-支配应用到道路网Skyline查询中以处理多属性数据对象，在实际应用中可以用来解决道路网环境下多目标查询和决策问题.方法主要包括道路网中约减数据集过程和K-支配检查过程.首先基于空间数据点构建网络Voronoi图，并对查询点建立查询凸包，通过网络Voronoi图的性质与查询区域的位置关系对数据集约减，从而优化数据集并且有效地减少查询点重复搜索的现象；然后对候选集的非空间属性进行K-支配检查得到道路网精炼集合；最后对精炼集合进行支配检查得到最终的空间Skyline集合.理论研究和实验表明所提出的方法具有较高的效率，可较好地处理道路网环境下K-支配空间Skyline查询问题.
- 空间Skyline查询 /
- 多目标决策 /
- Voronoi图 /
- 道路网 /
- 空间属性
Abstract: In order to make up for the shortcomings of the existing research results in dealing with the K-dominat space Skyline query problem in the road network environment, the method of the K-dominant space Skyline query in road network based on the network Voronoi diagram is proposed. This method applies K-dominant to the Skyline query of road network to deal with the multi-attribute data objects and can be used to solve the multi-objective decision problems in practical applications in the road network. The method mainly includes reduction data set process and K-dominant checking process in road network. Firstly, the Voronoi diagram is constructed based on spatial data points, and the query convex hull is built for query points. The Voronoi diagram of the data and the positional relationship of the query area are used to cut the data set. Thus the data set is optimized and the phenomenon of repeated search of query points is effectively reduced. Then, the refined set is obtained by K-dominant checking on the non-spatial attributes of the candidate set. Finally, the final spatial Skyline set is obtained by dominating the refined set.Theoretical research and experiments show that the proposed method has higher efficiency and can handle the K-dominant space Skyline query problem in the road network better.
- spatial Skyline query /
- multi-objective decision-making /
- Voronoi diagram /
- road network /
- spatial attribute

HTML全文

参考文献(0)

施引文献(17)

期刊类型引用(9)

1.	黄翔东，陈红红，甘霖. 基于频率-时间扩张密集网络的语音增强方法. 计算机研究与发展. 2023(07): 1628-1638 . 本站查看
2.	许春冬，徐琅，周滨. 结合优化U-Net和残差神经网络的单通道语音增强算法. 现代电子技术. 2022(09): 35-40 . 百度学术
3.	葛宛营，张天骐，范聪聪，张天. 噪声情况下采用稀疏非负矩阵分解与深度吸引子网络的人声分离算法. 声学学报. 2021(01): 55-66 . 百度学术
4.	GE Wanying，ZHANG Tianqi，FAN Congcong，ZHANG Tian. Monaural noisy speech separation combining sparse non-negative matrix factorization and deep attractor network. Chinese Journal of Acoustics. 2021(02): 266-280 . 必应学术
5.	王静红，梁丽娜，李昊康，周易. 基于注意力网络特征的社区发现算法. 山东大学学报(理学版). 2021(09): 1-12+20 . 百度学术
6.	张天骐，柏浩钧，叶绍鹏，刘鉴兴. 基于门控残差卷积编解码网络的单通道语音增强方法. 信号处理. 2021(10): 1986-1995 . 百度学术
7.	曹丽静. 语音增强技术研究综述. 河北省科学院学报. 2020(02): 30-36 . 百度学术
8.	张天骐，张晓艳，周琳，胡延平. 基于稀疏性的相位谱补偿语音增强算法. 信号处理. 2020(11): 1867-1876 . 百度学术
9.	时文华，张雄伟，邹霞，孙蒙. 利用深度全卷积编解码网络的单通道语音增强. 信号处理. 2019(04): 631-640 . 百度学术