Sakshi Agarwal, K. Narayanan, Manjira Sinha, Rohit Gupta, S. Eswaran, Tridib Mukherjee
{"title":"Decision Support Framework for Big Data Analytics","authors":"Sakshi Agarwal, K. Narayanan, Manjira Sinha, Rohit Gupta, S. Eswaran, Tridib Mukherjee","doi":"10.1109/SERVICES.2018.00040","DOIUrl":null,"url":null,"abstract":"Making design choices for big data systems is not trivial. If not planned out efficiently, keeping in mind the practical requirements, there's a possibility that the deployed system can lack important features to match up the application or it may contain over-sophisticated methods that incurs a large cost, but little increase in the efficiency, output. To equip the end user towards wise design choices, we have proposed a decision support framework for big data systems that can evaluate the suitability over numerous design combinations and outputs the one most efficient for the end-user requirement.","PeriodicalId":130225,"journal":{"name":"2018 IEEE World Congress on Services (SERVICES)","volume":"89 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 IEEE World Congress on Services (SERVICES)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SERVICES.2018.00040","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
Making design choices for big data systems is not trivial. If not planned out efficiently, keeping in mind the practical requirements, there's a possibility that the deployed system can lack important features to match up the application or it may contain over-sophisticated methods that incurs a large cost, but little increase in the efficiency, output. To equip the end user towards wise design choices, we have proposed a decision support framework for big data systems that can evaluate the suitability over numerous design combinations and outputs the one most efficient for the end-user requirement.