Abstract
Time range query is essential to facilitate a wide range of blockchain applications such as data provenance in the supply chain. Existing blockchain systems adopt the storage-consuming tree-based index structure for better query performance, however, fail to efficiently work for most blockchain nodes with limited resources. In this paper, we propose Anole, a lightweight and verifiable time range query mechanism, to present the feasibility of building up a learned-based index to achieve high performance and low storage costs on blockchain systems. The key idea of Anole is to exploit the temporal characteristics of blockchain data distribution and design a tailored lightweight index to reduce storage costs. Moreover, it uses a digital signature to guarantee the correctness and completeness of query results by considering the learned index’s error bounds, and applies batch verification to further improve verification performance. Experimental results demonstrate that Anole improves the query performance by up to 10\(\times \) and reduces the storage overhead by \(99.4\%\) compared with the state-of-the-art vChain+.
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Acknowledgement
This work was supported by National Key Research and Development Program of China under Grant No. 2021YFB2700700, Key Research and Development Program of Hubei Province No. 2021BEA164, National Natural Science Foundation of China (Grant No. 62072197), Key-Area Research and Development Program of Guangdong Province No. 2020B0101090005, Knowledge Innovation Program of Wuhan-Shuguang.
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Chang, J., Li, B., Xiao, J., Lin, L., Jin, H. (2023). Anole: A Lightweight and Verifiable Learned-Based Index for Time Range Query on Blockchain Systems. In: Wang, X., et al. Database Systems for Advanced Applications. DASFAA 2023. Lecture Notes in Computer Science, vol 13943. Springer, Cham. https://doi.org/10.1007/978-3-031-30637-2_34
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