{"title":"通过滑动窗口进行主存分析的延迟数据结构维护","authors":"Chang Ge, Lukasz Golab","doi":"10.1145/2513190.2513203","DOIUrl":null,"url":null,"abstract":"We address the problem of maintaining data structures used by memory-resident data warehouses that store sliding windows. We propose a framework that eagerly expires data from the sliding window to save space and/or satisfy data retention policies, but lazily maintains the associated data structures to reduce maintenance overhead. Using a dictionary as an example, we show that our framework enables maintenance algorithms that outperform existing approaches in terms of space overhead, maintenance overhead, and dictionary lookup overhead during query execution.","PeriodicalId":335396,"journal":{"name":"International Workshop on Data Warehousing and OLAP","volume":"7 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Lazy data structure maintenance for main-memory analytics over sliding windows\",\"authors\":\"Chang Ge, Lukasz Golab\",\"doi\":\"10.1145/2513190.2513203\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We address the problem of maintaining data structures used by memory-resident data warehouses that store sliding windows. We propose a framework that eagerly expires data from the sliding window to save space and/or satisfy data retention policies, but lazily maintains the associated data structures to reduce maintenance overhead. Using a dictionary as an example, we show that our framework enables maintenance algorithms that outperform existing approaches in terms of space overhead, maintenance overhead, and dictionary lookup overhead during query execution.\",\"PeriodicalId\":335396,\"journal\":{\"name\":\"International Workshop on Data Warehousing and OLAP\",\"volume\":\"7 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-10-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Workshop on Data Warehousing and OLAP\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/2513190.2513203\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Workshop on Data Warehousing and OLAP","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2513190.2513203","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Lazy data structure maintenance for main-memory analytics over sliding windows
We address the problem of maintaining data structures used by memory-resident data warehouses that store sliding windows. We propose a framework that eagerly expires data from the sliding window to save space and/or satisfy data retention policies, but lazily maintains the associated data structures to reduce maintenance overhead. Using a dictionary as an example, we show that our framework enables maintenance algorithms that outperform existing approaches in terms of space overhead, maintenance overhead, and dictionary lookup overhead during query execution.