Abstract: Spatio-temporal index is one of the key technologies for storage and management of spatio-temporal data. Index methods based on spatial filling curve (SFC) have drawn wide attention in recent year. However, the existing methods for the vector data mostly focus on the implementation of spatial index, which is difficult to take into account both the efficiency of time query and spatial query. For non-point elements (line elements and polygon elements), it is always difficult to determine the optimal index level. Therefore, this paper proposes an adaptive hierarchical spatio-temporal index construction method for vector data under peer-to-peer networks. Firstly, a joint coding of spatio-temporal information based on the combination strategy of partition key and sort key is proposed. Then, the spatio-temporal expression structure of point elements and non-point elements are designed. Finally, an adaptive multi-level tree is proposed to realize the spatio-temporal index (multi-level sphere 3, MLS3) based on the spatio-temporal characteristics of geographical entities. Experiments are carried out using actual data of trajectory (point elements), highway (line elements) and building (surface elements) data. By comparing with the XZ3 indexing algorithm proposed by GeoMesa, it is proved that the indexing method in this paper can effectively solve the problems of hierarchical division and spatio-temporal expression of non-point elements, and can effectively avoid storage hotspots while achieving efficient spatio-temporal retrieval.

Key words: spatio-temporal index, P2P networks, S2, multi-level tree, multi-level sphere three