P. Upadhyaya, Martina Unutzer, M. Balazinska, Dan Suciu, Hakan Hacıgümüş
{"title":"基于昂贵数据的平价分析","authors":"P. Upadhyaya, Martina Unutzer, M. Balazinska, Dan Suciu, Hakan Hacıgümüş","doi":"10.1145/2658840.2658844","DOIUrl":null,"url":null,"abstract":"In this paper, we outline steps towards supporting \"data analysis on a budget\" when operating in a setting where data must be bought, possibly periodically. We model the problem, and explore the design choices for analytic applications as well as potentially fruitful algorithmic techniques to reduce the cost of acquiring data. Simulations suggest that an order of magnitude improvements are possible.","PeriodicalId":135661,"journal":{"name":"Data4U '14","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2014-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Affordable Analytics on Expensive Data\",\"authors\":\"P. Upadhyaya, Martina Unutzer, M. Balazinska, Dan Suciu, Hakan Hacıgümüş\",\"doi\":\"10.1145/2658840.2658844\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we outline steps towards supporting \\\"data analysis on a budget\\\" when operating in a setting where data must be bought, possibly periodically. We model the problem, and explore the design choices for analytic applications as well as potentially fruitful algorithmic techniques to reduce the cost of acquiring data. Simulations suggest that an order of magnitude improvements are possible.\",\"PeriodicalId\":135661,\"journal\":{\"name\":\"Data4U '14\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Data4U '14\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/2658840.2658844\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Data4U '14","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2658840.2658844","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
In this paper, we outline steps towards supporting "data analysis on a budget" when operating in a setting where data must be bought, possibly periodically. We model the problem, and explore the design choices for analytic applications as well as potentially fruitful algorithmic techniques to reduce the cost of acquiring data. Simulations suggest that an order of magnitude improvements are possible.
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