{"title":"Parallel Computation of Skyline Queries","authors":"Adan Cosgaya-Lozano, A. Rau-Chaplin, N. Zeh","doi":"10.1109/HPCS.2007.25","DOIUrl":null,"url":null,"abstract":"Skyline queries have received considerable attention in the database community. The goal is to retrieve all records in a database that have the property that no other record is better according to all of a given set of criteria. While this problem has been well studied in the computational geometry literature, the solution of this problem in the database context requires techniques designed particularly to handle large amounts of data. In this paper, we show that parallel computing is an effective method to speed up the answering of skyline queries on large data sets. We also propose to preprocess the set of data points to quickly answer subsequent skyline queries on any subset of the dimensions.","PeriodicalId":354520,"journal":{"name":"21st International Symposium on High Performance Computing Systems and Applications (HPCS'07)","volume":"61 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2007-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"20","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"21st International Symposium on High Performance Computing Systems and Applications (HPCS'07)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/HPCS.2007.25","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 20
Abstract
Skyline queries have received considerable attention in the database community. The goal is to retrieve all records in a database that have the property that no other record is better according to all of a given set of criteria. While this problem has been well studied in the computational geometry literature, the solution of this problem in the database context requires techniques designed particularly to handle large amounts of data. In this paper, we show that parallel computing is an effective method to speed up the answering of skyline queries on large data sets. We also propose to preprocess the set of data points to quickly answer subsequent skyline queries on any subset of the dimensions.