扩展流数据立方体的GIS时空大数据组织方法

Big Data Organization Method of GIS Spatio-temporal Expanded the Streaming Data Cube

针对大数据GIS面临的大规模数据的多源异构动态性与数据存储优化等问题,本文开展大数据背景下的地理时空数据组织与模型研究,提出了一种基于流数据的可扩展立方体处理框架,在典型的流数据二维数据序列基础上,构建增加垂直方向的非结构数据立方体;结合立方体数据组织模型的定义和特征,探讨扩展关系型数据库与协同非关系型数据库的GIS时空大数据组织方法;通过扩展数据源、数据类型及数据操作等属性,突出多源异构地理时空大数据的时空关系和演变过程关系等特征,对地理时空大数据进行数据一体化组织、存储和分析;进而解决地理信息时空大数据的大体量、异构与动态性在GIS数据管理与分析方面的技术瓶颈,并且对GIS时空大数据的有效管理提供科学性方法和解决策略。

Aiming at the problem of multi-source heterogeneous dynamic and data storage optimization for large-scale data faced by big data GIS,this paper studies geographical spatio-temporal data organization and model under the background of big data,proposes an extensible cube handing framework based on stream data,on the typical two-dimensional data sequence of stream data,constructs a data cube that increases the vertical direction to manage the unstructured data.Combining the definition and characteristics of the data cube model,this paper discusses the organization method of GIS spatio-temporal big data by extending relational database and cooperating non-relational database. Simultaneously,by extending the data sources,data types and data manipulation,this paper highlights the characteristics of spatio-temporal relationship and evolution process in the multi-source heterogeneous big data,and then,the data of spatio-temporal big data is organized,stored and analyzed together.In the end,this paper addresses the technical bottleneck question of large volume,heterogeneity and dynamic for spatio-temporal big data in GIS data management and analysis. Also,it provides scientific methods and solutions to effectively manage GIS spatio-temporal big data.