Enabling Cross-Language Data Integration and Scalable Analytics in Decentralized Finance
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Authors
Flynn, Connor
Bennett, Kristin P.
Erickson, John S.
Green, Aaron
Seneviratne, Oshani
Issue Date
2024-01-22
Type
Article
Language
Keywords
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Abstract
With the agile development process of most academic and corporate entities, designing a robust computational back-end system that can support their ever-changing data needs is a constantly evolving challenge. We propose the implementation of a data and language-agnostic system design that handles different data schemes and sources while subsequently providing researchers and developers a way to connect to it that is supported by a vast majority of programming languages. To validate the efficacy of a system with this proposed architecture, we integrate various data sources throughout the decentralized finance (DeFi) space, specifically from DeFi lending protocols, retrieving tens of millions of data points to perform analytics through this system. We then access and process the retrieved data through several different programming languages (R-Lang, Python, and Java). Finally, we analyze the performance of the proposed architecture in relation to other high-performance systems and explore how this system performs under a high computational load.
Description
Full Citation
C. Flynn, K. P. Bennett, J. S. Erickson, A. Green and O. Seneviratne, "Enabling Cross-Language Data Integration and Scalable Analytics in Decentralized Finance," 2023 IEEE International Conference on Big Data (BigData), Sorrento, Italy, 2023, pp. 4290-4299, doi: 10.1109/BigData59044.2023.10386383.
Publisher
IEEE