ChainDash: Ad-Hoc区块链数据分析系统

IF 2.6 3区计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

Proceedings of the Vldb Endowment Pub Date : 2023-08-01 DOI:10.14778/3611540.3611611

Yushi Liu, Liwei Yuan, Zhihao Chen, Yekai Yu, Zhao Zhang, Cheqing Jin, Ying Yan

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引用次数: 0

摘要

由Web 3.0驱动并由区块链技术提供支持的数字资产应用程序的出现，导致对区块链特定图形分析的需求不断增长，以挖掘见解。然而，当前的区块链数据分析系统由于其低效的数据同步和缓慢的图形快照检索能力，无法在实时和过去的时间窗口上执行有效的临时图形分析。为了解决这些问题，我们提出了ChainDash，这是一个区块链数据分析系统，专门用于高度并行化的数据同步组件和检索优化的时态图存储。通过利用这些技术，ChainDash支持在任意时间窗口内对智能合约活动进行高效的临时图表分析。在演示中，我们展示了ChainDash的交互式可视化界面，与会者将在其中执行自定义查询，以对区块链数据进行临时图形分析。

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ChainDash: An Ad-Hoc Blockchain Data Analytics System

The emergence of digital asset applications, driven by Web 3.0 and powered by blockchain technology, has led to a growing demand for blockchain-specific graph analytics to unearth the insights. However, current blockchain data analytics systems are unable to perform efficient ad-hoc graph analytics over both live and past time windows due to their inefficient data synchronization and slow graph snapshots retrieval capability. To address these issues, we propose ChainDash, a blockchain data analytics system that dedicates a highly-parallelized data synchronization component and a retrieval-optimized temporal graph store. By leveraging these techniques, ChainDash supports efficient ad-hoc graph analytics of smart contract activities over arbitrary time windows. In the demonstration, we showcase the interactive visualization interfaces of ChainDash, where attendees will execute customized queries for ad-hoc graph analytics of blockchain data.

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来源期刊

Proceedings of the Vldb Endowment Computer Science-General Computer Science

CiteScore

7.70

自引率

0.00%

发文量

期刊介绍： The Proceedings of the VLDB (PVLDB) welcomes original research papers on a broad range of research topics related to all aspects of data management, where systems issues play a significant role, such as data management system technology and information management infrastructures, including their very large scale of experimentation, novel architectures, and demanding applications as well as their underpinning theory. The scope of a submission for PVLDB is also described by the subject areas given below. Moreover, the scope of PVLDB is restricted to scientific areas that are covered by the combined expertise on the submission’s topic of the journal’s editorial board. Finally, the submission’s contributions should build on work already published in data management outlets, e.g., PVLDB, VLDBJ, ACM SIGMOD, IEEE ICDE, EDBT, ACM TODS, IEEE TKDE, and go beyond a syntactic citation.