谭凯中,秦勃,何亚文

• 1:中国海洋大学信息科学与工程学院
• 2:中国石油大学(华东)海洋与空间信息学院

摘要(Abstract)：

海洋现象特征数据是一类带有过程性质的时空数据,具有多样性、多态性、海量性和时变性等特点,广泛服务于海洋灾害预报、船舶航行规划、台风路径追踪、海洋与大气环流等领域。本文根据海洋现象特征,建立海洋现象过程的对象表达和数据组织模型,设计三维空间海洋时空数据分片策略和R树空间数据索引方法,以提高海洋现象过程时空数据的查询效率。实验通过构建海洋现象过程时空数据分布式存储与并行检索系统测试算法的执行效率。实验结果表明,面向过程的海洋时空数据分布式并行检索算法能够实现数据的高效检索,验证了海洋现象过程对象数据组织模型的合理性,为研究海洋现象过程多要素的关联机制提供了有效的方法和理论基础。

关键词(KeyWords)： 面向过程;海洋时空数据;分布式检索;分布式存储;R树空间数据索引

Abstract:

Keywords:

基金项目(Foundation): 国家自然科学基金项目(41976184)资助~~

作者(Author): 谭凯中,秦勃,何亚文

DOI: 10.16441/j.cnki.hdxb.20200341

参考文献(References)：

• [1] Reitsma F,Albrecht J.Implementing a new data model for simulating processes[J].International Journal of Geographical Information Science,2005,19(10):1073-1090.
• [2] Yi J,Du Y,Liang F,et al.A representation framework for studying spatiotemporal changes and interactions of dynamic geographic phenomena[J].International Journal of Geographical Information Science,2014,28(5):1010-1027.
• [3] Yuan M.Representing complex geographic phenomena in GIS[J].American Cartographer,2001,28(2):83-96.
• [4] Claramunt C,Parent C,Theriault M.Design Patterns for Spatiotemporal Processes[C].Quebec:International Federation for Information Processing,1997.
• [5] 薛存金，周成虎，苏奋振，等.面向过程的时空数据模型研究[J].测绘学报，2010(1):95-101.Xue C,Zhou C,SU Fenzhen,et al.Research on process-oriented spatio-temporal data model[J].Acta Geodaetica et Cartographica Sinica,2010(1):95-101.
• [6] 薛存金，董庆.海洋时空过程数据模型及其原型系统构建研究[J].海洋通报，2012,31(6):667-674.Xue C,DONG Qing.Research on the marine spatio-temporal process data model and its prototype system construction[J].Marine Science Bulletin,2012,31(6):667-674.
• [7] Dorninger,Peter.XML Technologies and Geodata[C].Vienna Austria:International Symposium on Information and Conmunication Technologes in Urban and Spatial Planning and Impacts of ICT on Physical Space,2003:223-229.
• [8] Zhu Xu,Zhilin Li.A Schema Mapping Technique for XML-Based Semantic Geodata Translation[C].Fairfax,VA,USA:International Conference on Geoinformatics,IEEE,2009.
• [9] Yu J,Wu J,Sarwat M.Geo Spark:A Cluster Computing Framework for Processing Large-Scale Spatial Data[C].Seattle,Washington,USA:the 23rd Sigspatial International Conference,ACM,2015.
• [10] Yu J,Wu J,Sarwat M.A Demonstration of Geo Spark:A Cluster Computing Framework for Processing Big Spatial Data[C].Helsinki,Finland:IEEE International Conference on Data Engineering.IEEE,2016.
• [11] YuJ,Zhang Z,Sarwat M.Spatial data management in apache spark:The GeoSpark perspective and beyond[J].GeoInformatica,2019,23:37-78.
• [12] Schnitzer B,Leutenegger S T.Master-Client R-Trees:A New Parallel R-Tree Architecture[C].Denver,CO:Scientific and Statistical Database Management,1999.Eleventh International Conference on.IEEE,1999.
• [13] Xun L,Wenfeng Z.Parallel Spatial Index Algorithm Based on Hilbert Partition[C].Shiyang:International Conference on Computational and Information Sciences,2013:876-879.
• [14] Viet-Ngu Huynh Cong,Kang-Woo Lee,In-Hak Joo,et al.Improving the Quality of an R-Tree Using the Map-Reduce Framework[C].Seaul,Korea:International Conference on Multimedia and Ubiquitous Engineering International Conference on Future Information Technology,2017.
• [15] Yang J,Huang X.A hybrid spatial index for massive point cloud data management and visualization[J].Transactions in Gis,2014,18(S1):97-108.
• [16] Gang Z,Maomei W,Yi X,et al.Research on Spatial Index Structure of Massive Point Clouds Based on Hybrid Tree[C].Boston,MA,USA:International Conference on Big Data,2017:134-137.
• [17] Chen X,Zhang C,Ge B,et al.Spatio-Temporal Queries in HBase[C].Santa Clara,CA,USA:IEEE International Conference on Big Data.IEEE,2015.
• [18] Leutenegger S T,Lopez M A,Edgington J.STR:A Simple and Efficient Algorithm for R-Tree Packing[C].Birmingham U.K:International Conference on Data Engineering.IEEE,1997.

文章评论(Comment)：