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A Transparently-Secure and Robust Stock Data Supply Framework for Financial-Technology Applications

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12672))

Abstract

Recently, program trading has become the mainstream of financial information technology. The current FinTech applications mainly encounter three technical issues, including scalability, long-time retrieval, and security. Many small-and-medium companies seek economical solutions, while the three above properties are still met. In this paper, we propose Send-and-Subscribe (SaS) framework, aiming at providing a secure and robust financial stock data retrieval repository. In our survey to the related industries, the proposed novel framework is the first work on addressing the above three issues for financial computing areas. Finally, we conduct a set of experiments to validate the proposed framework on the real-world Taiwan stock data. The test results show the proposed framework indeed satisfies the security, scalability, and long-query requirements, comparing to existing solutions.

This work was supported by Ministry of Science and Technology (MOST) of Taiwan under Grants MOST 109-2221-E-006-199, 108-2221-E-034-015-MY2, and 109-2218-E-006-007. This work was financially supported by the “Intelligent Manufacturing Research Center” (iMRC) in NCKU from The Featured Areas Research Center Program within the framework of the Higher Education Sprout Project by the Ministry of Education in Taiwan.

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Correspondence to Lin-Yi Jiang or Cheng-Ju Kuo .

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Jiang, LY. et al. (2021). A Transparently-Secure and Robust Stock Data Supply Framework for Financial-Technology Applications. In: Nguyen, N.T., Chittayasothorn, S., Niyato, D., Trawiński, B. (eds) Intelligent Information and Database Systems. ACIIDS 2021. Lecture Notes in Computer Science(), vol 12672. Springer, Cham. https://doi.org/10.1007/978-3-030-73280-6_49

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  • DOI: https://doi.org/10.1007/978-3-030-73280-6_49

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-73279-0

  • Online ISBN: 978-3-030-73280-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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