Abstract
Spatio-temporal indexing is a key technique in spatio-temporal data storage and management. Indexing methods based on spatial filling curves are popular in research on the spatio-temporal indexing of vector data in the Not Relational (NoSQL) database. However, the existing methods mostly focus on spatial indexing, which makes it difficult to balance the efficiencies of time and space queries. In addition, for non-point elements (line and polygon elements), it remains difficult to determine the optimal index level. To address these issues, this paper proposes an adaptive construction method of hierarchical spatio-temporal index for vector data. Firstly, a joint spatio-temporal information coding based on the combination of the partition and sort key strategies is presented. Secondly, the multilevel expression structure of spatio-temporal elements consisting of point and non-point elements in the joint coding is given. Finally, an adaptive multi-level index tree is proposed to realize the spatio-temporal index (Multi-level Sphere 3, MLS3) based on the spatio-temporal characteristics of geographical entities. Comparison with the XZ3 index algorithm proposed by GeoMesa proved that the MLS3 indexing method not only reasonably expresses the spatio-temporal features of non-point elements and determines their optimal index level, but also avoids storage hotspots while achieving spatio-temporal retrieval with high efficiency.
Highlights
With the development of Internet technologies, peer-to-peer (P2P) networks are currently receiving considerable interest as they provide a decentralized architecture in which the shared resources on one node can be directly accessed by other peers without passing through intermediate entities [1]
To verify the validity and rationality of the Multi-Level Sphere 3 (MLS3) index proposed in this paper, comparative analysis with the XZ3 index was implemented with the same dataset and test environment
To address the issues existing in traditional spatio-temporal indexing, an adaptive hierarchical spatio-temporal index algorithm was developed in this study
Summary
With the development of Internet technologies, peer-to-peer (P2P) networks are currently receiving considerable interest as they provide a decentralized architecture in which the shared resources on one node can be directly accessed by other peers without passing through intermediate entities [1]. GeoSpark provides uniform grid, R-tree, Quad-Tree, and KDB-Tree (the combination of KD-tree and B-tree) spatial data indexing algorithm and it builds local spatial indexes on each Spark data partition to speed up the local computation [12] Such index structure always need long constructing time, high updating cost and the index consistency is difficult to maintain, it is not suitable for the spatio-temporal data which updated frequently in distributed environments. This paper focuses on the global spatio-temporal joint indexing of vector data under the P2P network by proposing a multilevel expression structure of spatio-temporal elements and an optimal index hierarchy determination algorithm considering the time granularity and spatial distribution feature of input files in the NoSQL database based on the S2 spatial index, which can achieve efficient spatio-temporal queries and stable query performance as well as a low index-maintaining cost.
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