TY - GEN
T1 - A Transparently-Secure and Robust Stock Data Supply Framework for Financial-Technology Applications
AU - Jiang, Lin Yi
AU - Kuo, Cheng Ju
AU - Wang, Yu Hsin
AU - Wu, Mu En
AU - Su, Wei Tsung
AU - Wang, Ding Chau
AU - Tang-Hsuan, O.
AU - Fu, Chi Luen
AU - Chen, Chao Chun
N1 - Funding Information:
Keywords: Financial technology (FinTech) · Security · Robustness · Financial data services · Granular computing 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.
Publisher Copyright:
© 2021, Springer Nature Switzerland AG.
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
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U2 - 10.1007/978-3-030-73280-6_49
DO - 10.1007/978-3-030-73280-6_49
M3 - Conference contribution
AN - SCOPUS:85104796034
SN - 9783030732790
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 616
EP - 629
BT - Intelligent Information and Database Systems - 13th Asian Conference, ACIIDS 2021, Proceedings
A2 - Nguyen, Ngoc Thanh
A2 - Chittayasothorn, Suphamit
A2 - Niyato, Dusit
A2 - Trawiński, Bogdan
PB - Springer Science and Business Media Deutschland GmbH
T2 - 13th Asian Conference on Intelligent Information and Database Systems, ACIIDS 2021
Y2 - 7 April 2021 through 10 April 2021
ER -