{"title":"构造r树的一种新方法","authors":"Shaoxi Li, De-peng Zhao, K. Bai","doi":"10.1109/ICCIS.2012.24","DOIUrl":null,"url":null,"abstract":"R-tree is a crucial technique for spatial index. However, coverage and overlap produced by traditional methods are big side effects. In order to minimize the overlap and reduce the coverage, this paper proposes a new R-tree construction method based on clustering algorithm which can minimize the overlap and reduce the coverage to a great extent. This method resolves effectively the clustering storage for the adjacent data, and reduces the overlap between spatial nodes. Comparisons and experiments are conducted and performances are evaluated for this method. The results show that this method has high efficiency in querying.","PeriodicalId":269967,"journal":{"name":"2012 Fourth International Conference on Computational and Information Sciences","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2012-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"A New Approach of R-tree Construction\",\"authors\":\"Shaoxi Li, De-peng Zhao, K. Bai\",\"doi\":\"10.1109/ICCIS.2012.24\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"R-tree is a crucial technique for spatial index. However, coverage and overlap produced by traditional methods are big side effects. In order to minimize the overlap and reduce the coverage, this paper proposes a new R-tree construction method based on clustering algorithm which can minimize the overlap and reduce the coverage to a great extent. This method resolves effectively the clustering storage for the adjacent data, and reduces the overlap between spatial nodes. Comparisons and experiments are conducted and performances are evaluated for this method. The results show that this method has high efficiency in querying.\",\"PeriodicalId\":269967,\"journal\":{\"name\":\"2012 Fourth International Conference on Computational and Information Sciences\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-08-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2012 Fourth International Conference on Computational and Information Sciences\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICCIS.2012.24\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 Fourth International Conference on Computational and Information Sciences","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCIS.2012.24","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
R-tree is a crucial technique for spatial index. However, coverage and overlap produced by traditional methods are big side effects. In order to minimize the overlap and reduce the coverage, this paper proposes a new R-tree construction method based on clustering algorithm which can minimize the overlap and reduce the coverage to a great extent. This method resolves effectively the clustering storage for the adjacent data, and reduces the overlap between spatial nodes. Comparisons and experiments are conducted and performances are evaluated for this method. The results show that this method has high efficiency in querying.