Title: Using knowledge graphs for enabling collaborative financial market data analytical processes

Authors: Bhushan Oza; Ali Behnaz

Addresses: Faculty of Engineering, School of Computer Science and Engineering, The University of New South Wales (UNSW), Kensington, Sydney, New South Wales 2052, Australia ' Faculty of Engineering, School of Computer Science and Engineering, The University of New South Wales (UNSW), Kensington, Sydney, New South Wales 2052, Australia

Abstract: Financial market data analysts are increasingly turning towards machine learning techniques for data analysis and making decisions. This paper explores how knowledge graphs can be used to improve collaborative financial market data analysis and make it more efficient and user friendly. This paper reviews the current state of financial market data analysis, the challenges in deploying machine learning models in industrial environments, and concepts such as developer operations (DevOps), machine learning operations (MLOps) and knowledge graphs. A software architecture comprising various roles, layers and services such as data engineers, data analysts, public users, application programming interfaces (APIs), workflows, analytics libraries, user experience (UX) layer, business layer, etc. is introduced and described in detail. Then, it discusses how knowledge graphs can be used to enhance collaborative financial market data analysis. Finally, experiments were conducted within the context of equity market data analytics, which revealed that the proposed solution was successful.

Keywords: knowledge graphs; financial markets; machine learning; data analysis; DevOps; machine learning operations; MLOps; software architecture.

DOI: 10.1504/IJCAST.2024.143884

International Journal of Complexity in Applied Science and Technology, 2024 Vol.1 No.2, pp.142 - 154

Received: 26 Jul 2024
Accepted: 26 Aug 2024
Published online: 12 Jan 2025 *

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