{"title":"技术视角：高效、可重复使用的懒惰采样","authors":"Thomas Neumann","doi":"10.1145/3665252.3665260","DOIUrl":null,"url":null,"abstract":"When interactively working with data, query latency is very important. In particular when ad-hoc queries are written in an explorative manner, it is essential to quickly get feedback in order to refine and correct the query based upon result values. This interactive use case is difficult to support if the underlying data is large, as analyzing large volumes of data is inherently expensive.","PeriodicalId":346332,"journal":{"name":"ACM SIGMOD Record","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Technical Perspective: Efficient and Reusable Lazy Sampling\",\"authors\":\"Thomas Neumann\",\"doi\":\"10.1145/3665252.3665260\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"When interactively working with data, query latency is very important. In particular when ad-hoc queries are written in an explorative manner, it is essential to quickly get feedback in order to refine and correct the query based upon result values. This interactive use case is difficult to support if the underlying data is large, as analyzing large volumes of data is inherently expensive.\",\"PeriodicalId\":346332,\"journal\":{\"name\":\"ACM SIGMOD Record\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-05-14\",\"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/10.1145/3665252.3665260\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACM SIGMOD Record","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3665252.3665260","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Technical Perspective: Efficient and Reusable Lazy Sampling
When interactively working with data, query latency is very important. In particular when ad-hoc queries are written in an explorative manner, it is essential to quickly get feedback in order to refine and correct the query based upon result values. This interactive use case is difficult to support if the underlying data is large, as analyzing large volumes of data is inherently expensive.
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