{"title":"Partial Selection Query in Peer-to-Peer Databases","authors":"F. Kashani, C. Shahabi","doi":"10.1109/ICDE.2006.111","DOIUrl":null,"url":null,"abstract":"In this paper, we propose DBSampler, a query execution mechanism to answer \"partial selection\" queries in peerto- peer databases. A partial selection query is an arbitrary selection query that is satisfied with a fraction \\in of the results; a universal operation with applications in database tuning, query optimization and approximate query processing in peer-to-peer databases. DBSampler is based on an epidemic dissemination algorithm. We model the epidemic dissemination as a percolation problem and by rigorous percolation analysis tune DBSampler per-query and on-thefly to answer partial queries correctly and efficiently. We verify the efficiency of DBSampler in terms of query cost and query time via extensive simulation.","PeriodicalId":6819,"journal":{"name":"22nd International Conference on Data Engineering (ICDE'06)","volume":"38 1","pages":"132-132"},"PeriodicalIF":0.0000,"publicationDate":"2006-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"22nd International Conference on Data Engineering (ICDE'06)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICDE.2006.111","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4
Abstract
In this paper, we propose DBSampler, a query execution mechanism to answer "partial selection" queries in peerto- peer databases. A partial selection query is an arbitrary selection query that is satisfied with a fraction \in of the results; a universal operation with applications in database tuning, query optimization and approximate query processing in peer-to-peer databases. DBSampler is based on an epidemic dissemination algorithm. We model the epidemic dissemination as a percolation problem and by rigorous percolation analysis tune DBSampler per-query and on-thefly to answer partial queries correctly and efficiently. We verify the efficiency of DBSampler in terms of query cost and query time via extensive simulation.