Angela Bonifati, Stefania Dumbrava, George Fletcher, Jan Hidders, Matthias Hofer, Wim Martens, Filip Murlak, Joshua Shinavier, Slawek Staworko, Dominik Tomaszuk
{"title":"Threshold Queries","authors":"Angela Bonifati, Stefania Dumbrava, George Fletcher, Jan Hidders, Matthias Hofer, Wim Martens, Filip Murlak, Joshua Shinavier, Slawek Staworko, Dominik Tomaszuk","doi":"https://dl.acm.org/doi/10.1145/3604437.3604452","DOIUrl":null,"url":null,"abstract":"<p>Threshold queries are an important class of queries that only require computing or counting answers up to a specified threshold value. To the best of our knowledge, threshold queries have been largely disregarded in the research literature, which is surprising considering how common they are in practice. We explore how such queries appear in practice and present a method that can be used to significantly improve the asymptotic bounds of their state-of-the-art evaluation algorithms. Our experimental evaluation of these methods shows order-of-magnitude performance improvements.</p>","PeriodicalId":501169,"journal":{"name":"ACM SIGMOD Record","volume":"252 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2023-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACM SIGMOD Record","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/https://dl.acm.org/doi/10.1145/3604437.3604452","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Threshold queries are an important class of queries that only require computing or counting answers up to a specified threshold value. To the best of our knowledge, threshold queries have been largely disregarded in the research literature, which is surprising considering how common they are in practice. We explore how such queries appear in practice and present a method that can be used to significantly improve the asymptotic bounds of their state-of-the-art evaluation algorithms. Our experimental evaluation of these methods shows order-of-magnitude performance improvements.
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