{"title":"数据汇总与分布式计算","authors":"Graham Cormode","doi":"10.1145/3212734.3212795","DOIUrl":null,"url":null,"abstract":"The notion of summarization is to provide a compact representation of data which approximately captures its essential characteristics. If such summaries can be created, they can lead to efficient distributed algorithms which exchange summaries in order to compute a desired function. In this talk, I'll describe recent efforts in this direction for problems inspired by machine learning: building graphical models over evolving, distributed training examples, and solving robust regression problems over large, distributed data sets.","PeriodicalId":198284,"journal":{"name":"Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2018-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Data Summarization and Distributed Computation\",\"authors\":\"Graham Cormode\",\"doi\":\"10.1145/3212734.3212795\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The notion of summarization is to provide a compact representation of data which approximately captures its essential characteristics. If such summaries can be created, they can lead to efficient distributed algorithms which exchange summaries in order to compute a desired function. In this talk, I'll describe recent efforts in this direction for problems inspired by machine learning: building graphical models over evolving, distributed training examples, and solving robust regression problems over large, distributed data sets.\",\"PeriodicalId\":198284,\"journal\":{\"name\":\"Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-07-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3212734.3212795\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3212734.3212795","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
The notion of summarization is to provide a compact representation of data which approximately captures its essential characteristics. If such summaries can be created, they can lead to efficient distributed algorithms which exchange summaries in order to compute a desired function. In this talk, I'll describe recent efforts in this direction for problems inspired by machine learning: building graphical models over evolving, distributed training examples, and solving robust regression problems over large, distributed data sets.
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