S. Ji, Beom Seok Lee, K. I. Kang, Sung Gook Kim, Cheol-Won Lee, Oh-young Song, J. Choeh, R. Baik, S. Baik
{"title":"A Study on the Generation of OLAP Data Cube Based on 3D Visualization Interaction","authors":"S. Ji, Beom Seok Lee, K. I. Kang, Sung Gook Kim, Cheol-Won Lee, Oh-young Song, J. Choeh, R. Baik, S. Baik","doi":"10.1109/ICCSA.2011.40","DOIUrl":null,"url":null,"abstract":"This paper proposes a new method of creating 3D visual data cubes for high volume/dimension OLAP data analysis with intuitive region selection. Previous methods construct data cubes directly from a data warehouse and build table format cubes with multi-dimensional attributes, in order to specify target ranges for analysis. However, it is a difficult task to select appropriate attributes and their ranges from high cardinality of dimensions with hierarchical structure. The new method reduces the number of dimensions according to the levels of relationship, then confines analysis target ranges with intuitive 3D graphical interface to build an analysis target cube.","PeriodicalId":428638,"journal":{"name":"2011 International Conference on Computational Science and Its Applications","volume":"11 8","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2011-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2011 International Conference on Computational Science and Its Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCSA.2011.40","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
This paper proposes a new method of creating 3D visual data cubes for high volume/dimension OLAP data analysis with intuitive region selection. Previous methods construct data cubes directly from a data warehouse and build table format cubes with multi-dimensional attributes, in order to specify target ranges for analysis. However, it is a difficult task to select appropriate attributes and their ranges from high cardinality of dimensions with hierarchical structure. The new method reduces the number of dimensions according to the levels of relationship, then confines analysis target ranges with intuitive 3D graphical interface to build an analysis target cube.
京公网安备 11010802042870号
京ICP备2023020795号-1
分享
求助内容：
应助结果提醒方式：
扫码关注我们
