LAUSR

Abstract Geographic space is abstracted into a meta-semantic object (MSO), which is regarded as the smallest storage unit according to the semantic constraint principle and the characteristics of spatial-temporal information. An efficient organization and storage method is proposed by adopting the strong expansibility and real-time reading and writing characteristics of HBase through the encapsulation of MSO spatial-temporal information. This paper focuses on the classification and abstraction of geographic spatial-temporal information. The HBase platform is used as a carrier for the storage of massive spatial-temporal data. An HBase storage model is built by constructing a spatial-temporal data table and designing the physical structure of the MSO. By comparing the query time of the proposed model with that of the traditional spatial-temporal data model, the experimental results show that the query speed of the proposed model is higher. This provides a new idea for the organization and storage of massive spatial-temporal data.