S. Sakr, T. Rabl, Martin Hirzel, Paris Carbone, M. Strohbach
{"title":"Dagstuhl大数据流处理研讨会","authors":"S. Sakr, T. Rabl, Martin Hirzel, Paris Carbone, M. Strohbach","doi":"10.1145/3316416.3316426","DOIUrl":null,"url":null,"abstract":"Stream processing can generate insights from big data in real time as it is being produced. This paper reports findings from a 2017 seminar on big stream processing, focusing on applications, systems, and languages.","PeriodicalId":21740,"journal":{"name":"SIGMOD Rec.","volume":"126 1","pages":"36-39"},"PeriodicalIF":0.0000,"publicationDate":"2019-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Dagstuhl Seminar on Big Stream Processing\",\"authors\":\"S. Sakr, T. Rabl, Martin Hirzel, Paris Carbone, M. Strohbach\",\"doi\":\"10.1145/3316416.3316426\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Stream processing can generate insights from big data in real time as it is being produced. This paper reports findings from a 2017 seminar on big stream processing, focusing on applications, systems, and languages.\",\"PeriodicalId\":21740,\"journal\":{\"name\":\"SIGMOD Rec.\",\"volume\":\"126 1\",\"pages\":\"36-39\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-02-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"SIGMOD Rec.\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3316416.3316426\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"SIGMOD Rec.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3316416.3316426","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Stream processing can generate insights from big data in real time as it is being produced. This paper reports findings from a 2017 seminar on big stream processing, focusing on applications, systems, and languages.
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
