{"title":"Fast approach for automatic data retrieval using R programming language","authors":"Tran Duc Chung, R. Ibrahim, S. Hassan, N. Rosli","doi":"10.1109/ROMA.2016.7847824","DOIUrl":null,"url":null,"abstract":"The development of big data analytics in recent years has enabled wide use of analysis tools such as R in various industries including robotics and automation. Its most recent reported application for data analysis is limited to only a small dataset and the processing time was not reported. Thus, this work proposes a fast approach for automatic data retrieval using R, a powerful programming language for statistical and big data analysis. The developed algorithm performs data retrieval using a source file and a map file as inputs and produces a desired output file. Based on the experiment results on a real dataset, significantly low processing time can be achieved using the approach. In addition, the developed algorithm is general and thus can be applied in various similar data retrieval applications such as pooling sub-dataset in manufacturing environment involving the use of robotics and automation.","PeriodicalId":409977,"journal":{"name":"2016 2nd IEEE International Symposium on Robotics and Manufacturing Automation (ROMA)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 2nd IEEE International Symposium on Robotics and Manufacturing Automation (ROMA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ROMA.2016.7847824","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 5
Abstract
The development of big data analytics in recent years has enabled wide use of analysis tools such as R in various industries including robotics and automation. Its most recent reported application for data analysis is limited to only a small dataset and the processing time was not reported. Thus, this work proposes a fast approach for automatic data retrieval using R, a powerful programming language for statistical and big data analysis. The developed algorithm performs data retrieval using a source file and a map file as inputs and produces a desired output file. Based on the experiment results on a real dataset, significantly low processing time can be achieved using the approach. In addition, the developed algorithm is general and thus can be applied in various similar data retrieval applications such as pooling sub-dataset in manufacturing environment involving the use of robotics and automation.