关键词: 区块链, 查询优化, 索引模型, 布隆过滤器, 聚类算法

Abstract: Aiming at the problems of low query efficiency and limited query types in on-chain data querying of blockchain systems, an inter-block index model was proposed. Firstly, for discrete attributes within blocks, Inverted Bloom Filters (IBFS) index was introduced. When querying data using IBFS, it allows for locating target block in O(1) time complexity without full blockchain traversal. Secondly, for continuous attributes, clustering algorithm was employed to calculate fine-grained data distribution intervals. Dual-Layer Clustering Chain (DLCC) index was constructed based on maximum/minimum values and data distribution intervals, enabling the filtering of more non-target blocks during queries. Then, based on the proposed index model, various query algorithms were designed and implemented. Experimental results show that compared with Tree Bloom Filter index, the storage space of IBFS index is reduced by 51.0%, and time to locate target blocks is shortened by 75.9%. Compared with Start-End interval index, the number of blocks located by DLCC index during range query is reduced by 55.5%.

Key words: Blockchain, query optimization, index model, Bloom filter, clustering algorithm