任意维无约束约束的k-遗憾查询算法

Min Xie, R. C. Wong, J. Li, Cheng Long, Ashwin Lall
{"title":"任意维无约束约束的k-遗憾查询算法","authors":"Min Xie, R. C. Wong, J. Li, Cheng Long, Ashwin Lall","doi":"10.1145/3183713.3196903","DOIUrl":null,"url":null,"abstract":"Extracting interesting tuples from a large database is an important problem in multi-criteria decision making. Two representative queries were proposed in the literature: top- k queries and skyline queries. A top- k query requires users to specify their utility functions beforehand and then returns k tuples to the users. A skyline query does not require any utility function from users but it puts no control on the number of tuples returned to users. Recently, a k-regret query was proposed and received attention from the community because it does not require any utility function from users and the output size is controllable, and thus it avoids those deficiencies of top- k queries and skyline queries. Specifically, it returns k tuples that minimize a criterion called the maximum regret ratio . In this paper, we present the lower bound of the maximum regret ratio for the k -regret query. Besides, we propose a novel algorithm, called SPHERE, whose upper bound on the maximum regret ratio is asymptotically optimal and restriction-free for any dimensionality, the best-known result in the literature. We conducted extensive experiments to show that SPHERE performs better than the state-of-the-art methods for the k -regret query.","PeriodicalId":20430,"journal":{"name":"Proceedings of the 2018 International Conference on Management of Data","volume":"44 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2018-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"39","resultStr":"{\"title\":\"Efficient k-Regret Query Algorithm with Restriction-free Bound for any Dimensionality\",\"authors\":\"Min Xie, R. C. Wong, J. Li, Cheng Long, Ashwin Lall\",\"doi\":\"10.1145/3183713.3196903\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Extracting interesting tuples from a large database is an important problem in multi-criteria decision making. Two representative queries were proposed in the literature: top- k queries and skyline queries. A top- k query requires users to specify their utility functions beforehand and then returns k tuples to the users. A skyline query does not require any utility function from users but it puts no control on the number of tuples returned to users. Recently, a k-regret query was proposed and received attention from the community because it does not require any utility function from users and the output size is controllable, and thus it avoids those deficiencies of top- k queries and skyline queries. Specifically, it returns k tuples that minimize a criterion called the maximum regret ratio . In this paper, we present the lower bound of the maximum regret ratio for the k -regret query. Besides, we propose a novel algorithm, called SPHERE, whose upper bound on the maximum regret ratio is asymptotically optimal and restriction-free for any dimensionality, the best-known result in the literature. We conducted extensive experiments to show that SPHERE performs better than the state-of-the-art methods for the k -regret query.\",\"PeriodicalId\":20430,\"journal\":{\"name\":\"Proceedings of the 2018 International Conference on Management of Data\",\"volume\":\"44 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-05-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"39\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 2018 International Conference on Management of Data\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3183713.3196903\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2018 International Conference on Management of Data","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3183713.3196903","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 39

摘要

从大型数据库中提取感兴趣的元组是多准则决策中的一个重要问题。文献中提出了两种具有代表性的查询:top- k查询和skyline查询。top- k查询要求用户事先指定他们的实用函数,然后返回k个元组给用户。skyline查询不需要用户的任何实用函数,但它无法控制返回给用户的元组的数量。最近,由于k-后悔查询不需要用户的任何效用函数,并且输出大小可控,从而避免了top- k查询和skyline查询的不足,而被提出并受到了社区的关注。具体来说,它返回k个元组,这些元组最小化一个称为最大后悔率的标准。本文给出了k -后悔查询的最大后悔率的下界。此外,我们提出了一种新的算法,称为SPHERE,其最大后悔率的上界对于任何维度都是渐近最优的,并且没有限制,这是文献中最著名的结果。我们进行了大量的实验,以表明SPHERE比最先进的k -后悔查询方法表现得更好。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Efficient k-Regret Query Algorithm with Restriction-free Bound for any Dimensionality
Extracting interesting tuples from a large database is an important problem in multi-criteria decision making. Two representative queries were proposed in the literature: top- k queries and skyline queries. A top- k query requires users to specify their utility functions beforehand and then returns k tuples to the users. A skyline query does not require any utility function from users but it puts no control on the number of tuples returned to users. Recently, a k-regret query was proposed and received attention from the community because it does not require any utility function from users and the output size is controllable, and thus it avoids those deficiencies of top- k queries and skyline queries. Specifically, it returns k tuples that minimize a criterion called the maximum regret ratio . In this paper, we present the lower bound of the maximum regret ratio for the k -regret query. Besides, we propose a novel algorithm, called SPHERE, whose upper bound on the maximum regret ratio is asymptotically optimal and restriction-free for any dimensionality, the best-known result in the literature. We conducted extensive experiments to show that SPHERE performs better than the state-of-the-art methods for the k -regret query.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Meta-Dataflows: Efficient Exploratory Dataflow Jobs Columnstore and B+ tree - Are Hybrid Physical Designs Important? Demonstration of VerdictDB, the Platform-Independent AQP System Efficient Selection of Geospatial Data on Maps for Interactive and Visualized Exploration Session details: Keynote1
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1