Michael Grossniklaus, D. Maier, James Miller, Sharmadha Moorthy, K. Tufte
{"title":"Frames","authors":"Michael Grossniklaus, D. Maier, James Miller, Sharmadha Moorthy, K. Tufte","doi":"10.1145/2933267.2933304","DOIUrl":null,"url":null,"abstract":"Traditional Data Stream Management Systems (DSMS) segment data streams using windows that are defined either by a time interval or a number of tuples. Such windows are fixed---the definition unvarying over the course of a stream---and are defined based on external properties unrelated to the data content of the stream. However, streams and their content do vary over time---the rate of a data stream may vary or the data distribution of the content may vary. The mismatch between a fixed stream segmentation and a variable stream motivates the need for a more flexible, expressive and physically independent stream segmentation. We introduce a new stream segmentation technique, called frames. Frames segment streams based on data content. We present a theory and implementation of frames and show the utility of frames for a variety of applications.","PeriodicalId":277061,"journal":{"name":"Proceedings of the 10th ACM International Conference on Distributed and Event-based Systems","volume":"24 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"25","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 10th ACM International Conference on Distributed and Event-based Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2933267.2933304","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 25
Abstract
Traditional Data Stream Management Systems (DSMS) segment data streams using windows that are defined either by a time interval or a number of tuples. Such windows are fixed---the definition unvarying over the course of a stream---and are defined based on external properties unrelated to the data content of the stream. However, streams and their content do vary over time---the rate of a data stream may vary or the data distribution of the content may vary. The mismatch between a fixed stream segmentation and a variable stream motivates the need for a more flexible, expressive and physically independent stream segmentation. We introduce a new stream segmentation technique, called frames. Frames segment streams based on data content. We present a theory and implementation of frames and show the utility of frames for a variety of applications.