{"title":"Closest Interval Join Using MapReduce","authors":"Qiang Zhang, Andy He, Chris Liu, Eric Lo","doi":"10.1109/DSAA.2016.39","DOIUrl":null,"url":null,"abstract":"The closest interval join problem is to find all the closest intervals between two interval sets R and S. Applications of closest interval join include bioinformatics and other data science. Interval data can be very large and continue to increase in size due to the advancement of data acquisition technology. In this paper, we present efficient MapReduce algorithms to compute closest interval join. Experiments based on both real and synthetic interval data demonstrated that our algorithms are efficient.","PeriodicalId":193885,"journal":{"name":"2016 IEEE International Conference on Data Science and Advanced Analytics (DSAA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 IEEE International Conference on Data Science and Advanced Analytics (DSAA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/DSAA.2016.39","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4
Abstract
The closest interval join problem is to find all the closest intervals between two interval sets R and S. Applications of closest interval join include bioinformatics and other data science. Interval data can be very large and continue to increase in size due to the advancement of data acquisition technology. In this paper, we present efficient MapReduce algorithms to compute closest interval join. Experiments based on both real and synthetic interval data demonstrated that our algorithms are efficient.
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