flexm: b区块链中高效和可验证的索引管理

IF 8.9 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Binhong Li;Licheng Lin;Shijie Zhang;Jianliang Xu;Jiang Xiao;Bo Li;Hai Jin
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引用次数: 0

摘要

基于区块链的查询具有可追溯性和数据来源,在众多应用中越来越受欢迎和广泛采用。然而,现有的基于索引的查询方法仅在静态区块链查询工作负载下有效,其中查询属性或类型必须固定。事实证明,由于构建时间过长和存储消耗过大,为动态工作负载构建高效索引尤其具有挑战性。本文提出了首个高效、可验证的区块链动态查询索引管理系统FlexIM。FlexIM的关键创新是揭示区块链的固有特征,即数据分布和块访问频率,然后利用强化学习技术在不同工作负载下优化选择索引。此外,我们通过利用根默克尔树(RMT)和布隆过滤器默克尔树(BMT)来增强和促进低存储开销的可验证性。我们的综合评估表明,与现实世界的比特币数据集相比,FlexIM实现了26.5%的加速,而平均消耗的存储空间减少了94.2%,优于最先进的区块链查询机制vChain+。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
FlexIM: Efficient and Verifiable Index Management in Blockchain
Blockchain-based query with its traceability and data provenance has become increasingly popular and widely adopted in numerous applications. Yet existing index-based query approaches are only efficient under static blockchain query workloads where the query attribute or type must be fixed. It turns out to be particularly challenging to construct an efficient index for dynamic workloads due to prohibitively long construction time and excessive storage consumption. In this paper, we present FlexIM, the first efficient and verifiable index management system for blockchain dynamic queries. The key innovation in FlexIM is to uncover the inherent characteristics of blockchain, i.e., data distribution and block access frequency, and then to optimally choose the index by utilizing reinforcement learning technique under varying workloads. In addition, we enhance and facilitate verifiability with low storage overhead by leveraging Root Merkle Tree (RMT) and Bloom Filter Merkle Tree (BMT). Our comprehensive evaluations demonstrate that FlexIM outperforms the state-of-the-art blockchain query mechanism, vChain+, by achieving a 26.5% speedup while consuming 94.2% less storage, on average, over real-world Bitcoin datasets.
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来源期刊
IEEE Transactions on Knowledge and Data Engineering
IEEE Transactions on Knowledge and Data Engineering 工程技术-工程:电子与电气
CiteScore
11.70
自引率
3.40%
发文量
515
审稿时长
6 months
期刊介绍: The IEEE Transactions on Knowledge and Data Engineering encompasses knowledge and data engineering aspects within computer science, artificial intelligence, electrical engineering, computer engineering, and related fields. It provides an interdisciplinary platform for disseminating new developments in knowledge and data engineering and explores the practicality of these concepts in both hardware and software. Specific areas covered include knowledge-based and expert systems, AI techniques for knowledge and data management, tools, and methodologies, distributed processing, real-time systems, architectures, data management practices, database design, query languages, security, fault tolerance, statistical databases, algorithms, performance evaluation, and applications.
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