{"title":"容错分布式流处理系统","authors":"M. Gorawski, Pawel Marks","doi":"10.1109/DEXA.2006.61","DOIUrl":null,"url":null,"abstract":"Real-time data processing systems are more and more popular nowadays. Data warehouses not only collect terabytes of data, they also process endless data streams. To support such a situation, a data extraction process must become a continuous process also. Here a problem of a failure resistance arises. It is important not only to process a set of data on time, even more important is not to lose any data when a failure occurs. We achieve this by applying a redundant distributed stream processing. In this paper, we present a fault-tolerant system designed for processing data streams originating from geographically distributed sources","PeriodicalId":282986,"journal":{"name":"17th International Workshop on Database and Expert Systems Applications (DEXA'06)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2006-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"14","resultStr":"{\"title\":\"Fault-Tolerant Distributed Stream Processing System\",\"authors\":\"M. Gorawski, Pawel Marks\",\"doi\":\"10.1109/DEXA.2006.61\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Real-time data processing systems are more and more popular nowadays. Data warehouses not only collect terabytes of data, they also process endless data streams. To support such a situation, a data extraction process must become a continuous process also. Here a problem of a failure resistance arises. It is important not only to process a set of data on time, even more important is not to lose any data when a failure occurs. We achieve this by applying a redundant distributed stream processing. In this paper, we present a fault-tolerant system designed for processing data streams originating from geographically distributed sources\",\"PeriodicalId\":282986,\"journal\":{\"name\":\"17th International Workshop on Database and Expert Systems Applications (DEXA'06)\",\"volume\":\"17 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2006-09-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"14\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"17th International Workshop on Database and Expert Systems Applications (DEXA'06)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/DEXA.2006.61\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"17th International Workshop on Database and Expert Systems Applications (DEXA'06)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/DEXA.2006.61","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Fault-Tolerant Distributed Stream Processing System
Real-time data processing systems are more and more popular nowadays. Data warehouses not only collect terabytes of data, they also process endless data streams. To support such a situation, a data extraction process must become a continuous process also. Here a problem of a failure resistance arises. It is important not only to process a set of data on time, even more important is not to lose any data when a failure occurs. We achieve this by applying a redundant distributed stream processing. In this paper, we present a fault-tolerant system designed for processing data streams originating from geographically distributed sources