{“状态”:“确定”,“消息类型”:“工作”,“信息版本”:“1.0.0”,“邮件”:{“索引”:{“日期-部分”:[[2024,7,13]],“日期-时间”:“2024-07-13T00:13:59Z”,“时间戳”:1720829639364},“引用-计数”:45,“发布者”:“计算机协会(ACM)”,“问题”:“4”,“内容-域”:{-“域”:[],“交叉标记限制”:false},”short集装箱装载机”:[“Proc.VLDB Endow.”],“published-print”:{“date-parts”:[[2020,12]]},“abstract”:“我们提出了Stacked Filters,这是一种新的概率过滤器,它与查询无关过滤器(如Bloom和Cuckoo过滤器)类似,具有快速性和鲁棒性,同时具有与基于分类器的过滤器(如Learned Filters)类似的低误报率和大小。其核心思想是,堆叠过滤器结合了有关经常查询的不存在值的工作负载知识。他们没有学习,而是在结构上使用散列和几个有序的过滤层来整合这些知识,为数据和频繁的负片编制索引。堆叠过滤器还可以实时收集工作负载知识并自适应构建过滤器。我们的实验表明,对于给定的内存预算,堆叠过滤器实现的端到端查询吞吐量比工作负载的最佳替代方案(不确定查询或基于分类器的过滤器)高出130倍,具体取决于数据的位置(SSD或HDD)<\/jats:p>“,”DOI“:”10.14778\/3436905.3436919“,”type“:”journal-article“,”created“:{”date-parts“:[2021,2,22]],”date-time“:”2021-02-22T17:23:50Z“,”timestamp“:1614014630000},”page“:“:[{”given“:”Kyle“,”family“:”Deeds“,”sequence“:”first“,”affiliation“:[{“name”:“Harvard University”}]},{“given”:“Brian”,“family”:“Hentschel”,“sequence”:“additional”,“affiliation:[{”name“:”Harvard University“}]}.,{”given“:”Stratos“,”family“:”Idrees“,”sequence“:”additional 1,2,22]]},“引用”:[{“key“:”e_1_1_1“,”unstructured“:”Shalla secure services kg.http:\/\/www.shallalist.de.Shalla secure services kg。http://www.shallalist.dle.“},{“key”:“e_2_1_2_1”,“unstructure”:“前1000万网站:Openpagerank.”https:\/\www.domcop.com\/Openpagerank\/what-is-Openpagerank\“”前1000万网站:Openpagerank\“https:\/\/www.domcop.com\/openpagerank\/what-is-openpagerank\”。“},{”key“:”e_1_1_3_1“,”volume-title“:”Firehose流媒体基准测试“,”author“:”Anderson K.“,”year“:”2015“}”,{“key”:“e_2_1_4_1”,”doi-asserted-by“:”publisher“,“doi”:“10.14778\/2350229.2350275”},“{”密钥“:”d_2_5_1“”,“doi-assert-by”:“publisher”,“doi:”10.1145\/362692“}“:”e_1_2_1_6_1“,”doi-asserted-by“:”publisher“,“doi”:“10.14778\/3213880.3213884“},{“key”:“e_1_1_7_1”,“首页”:“636”,“volume-title”:“互联网数学”,“author”:“Broder A.”,“year”:“2002”},}“key:”e_2_1_8_1“,”首页“:”2304“,”volume-tite“:”加权bloom过滤器“,”author“:”Bruck J.“,“year:”2006“}”,{”key“:”e_1_ 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