{"title":"Quad-splitting algorithm for a window query on a Hilbert curve","authors":"Chen-Chang Wu, Ye-In Chang","doi":"10.1049/IET-IPR.2008.0155","DOIUrl":null,"url":null,"abstract":"Space-filling curves, particularly, Hilbert curves, have been extensively used to maintain spatial locality of multi-dimensional data in a wide variety of applications. A window query is an important query operation in spatial (image) databases. Given a Hilbert curve, a window query reports its corresponding orders without the need to decode all the points inside this window into the corresponding Hilbert orders. Given a query window of size p times q on a Hilbert curve of size T times T , Chung et al. have proposed an algorithm for decomposing a window into the corresponding Hilbert orders, which needs O ( n log T ) time, where n = max ( p , q ). By employing the properties of Hilbert curves, the authors present an efficient algorithm, named as Quad-Splitting, for decomposing a window into the corresponding Hilbert orders on a Hilbert curve without individual sorting and merging steps. Although the proposed algorithm also takes O ( n log T ) time, it does not perform individual sorting and merging steps which are needed in Chung et al. 's algorithm. Therefore experimental results show that the Quad-Splitting algorithm outperforms Chung et al. 's algorithm.","PeriodicalId":13486,"journal":{"name":"IET Image Process.","volume":"17 1","pages":"299-311"},"PeriodicalIF":0.0000,"publicationDate":"2009-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IET Image Process.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1049/IET-IPR.2008.0155","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
Space-filling curves, particularly, Hilbert curves, have been extensively used to maintain spatial locality of multi-dimensional data in a wide variety of applications. A window query is an important query operation in spatial (image) databases. Given a Hilbert curve, a window query reports its corresponding orders without the need to decode all the points inside this window into the corresponding Hilbert orders. Given a query window of size p times q on a Hilbert curve of size T times T , Chung et al. have proposed an algorithm for decomposing a window into the corresponding Hilbert orders, which needs O ( n log T ) time, where n = max ( p , q ). By employing the properties of Hilbert curves, the authors present an efficient algorithm, named as Quad-Splitting, for decomposing a window into the corresponding Hilbert orders on a Hilbert curve without individual sorting and merging steps. Although the proposed algorithm also takes O ( n log T ) time, it does not perform individual sorting and merging steps which are needed in Chung et al. 's algorithm. Therefore experimental results show that the Quad-Splitting algorithm outperforms Chung et al. 's algorithm.