Spatial LSM Tree for Indexing Blockchain-based Geospatial Point Data

Minjun Seo, Taehyeon Kwon, Sungwon Jung

http://doi.org/10.5626/JOK.2022.49.10.898

Blockchain technology is attracting attention for its high usability in various fields such as IoT and healthcare, and it is being used as an alternative to distributed databases. Despite their high usability for blockchain, the techniques for efficiently indexing blockchain-based geospatial data have not been studied much until now. Therefore, in this paper, we propose a spatial LSM tree indexing method that reduces the I/O cost when a block of geospatial point data is inserted into a blockchain by reflecting the write-intensive features of the blockchain. The proposed method linearizes geospatial data through Geohash on the blockchain where a large scale of real-time updates occur. It also minimizes the I/O cost when processing a range query and inserting data into the blockchain by taking the spatial proximity of the point data into account. Also, we propose a spatial filter to reduce unnecessary traversal of spatial LSM tree for processing geospatial point data range queries.