Implementing Automation with Business Process Model and Notation (BPMN) for Margin Call Workflow
Keywords:
Business Process Model and Notation (BPMN), Margin Call Workflow, Automation, Microservices Architecture, Audit TrailAbstract
Businesses looking to capitalize on flexibility, resilience, and performance have embraced adopting multi-cloud infrastructure, especially the combination of Amazon Web Services (AWS) and Microsoft Azure. This helps prevent organizations from becoming locked onto a single cloud vendor and lets them take advantage of other providers' most effective cloud offerings for scaling and workload management. Using example code, this report outlines the methods for merging AWS and Azure into seamless multi-cloud architecture by covering important architectural design, security, and cost management elements. Advanced technologies such as artificial intelligence (AI) and machine learning (ML) are emphasized as the next big growth for automating resource management, increasing performance and minimizing costs through operation. Furthermore, the report looks at businesses' struggles when joining multi-cloud surroundings, such as PC data interoperability, security, and compliance between several platforms. Multi-cloud strategies are used in industry-specific use cases in areas like healthcare, finance, and retail, which leverages the ability of this strategy to cater to sector-specific concerns to improve operational efficiency. With the cloud landscape transforming rapidly, the report provides recommendations to organizations adopting or optimizing their multi-cloud strategy to stay agile, cost-effective and compliant in a world that is going digital. Automated and AI-optimized cloud management emerges as a future of multi-cloud infrastructure.
References
Alzubaidi, A., Mitra, K., Patel, P., & Solaiman, E. (2020, August). A blockchain-based approach for assessing compliance with sla-guaranteed iot services. In 2020 IEEE International Conference on Smart Internet of Things (SmartIoT) (pp. 213-220). IEEE.
Bernardos, S., Fernández-Izquierdo, A., García-Castro, R., Andriopoulos, P., Baňas, V., Bosché, F., ... & Meusburger, E. B. (2021). D3. 1–survey of existing models, ontologies and associated standardization efforts. Technical report., COGITO.
Chamangwa, J. (2022). An analysis of the implementation of automated credit risk management strategy on loan recovery rate among Standard bank Zambia clients in Lusaka district, Zambia (Doctoral dissertation, The University of Zambia).
Chavan, A. (2021). Eventual consistency vs. strong consistency: Making the right choice in microservices. International Journal of Software and Applications, 14(3), 45-56. https://ijsra.net/content/eventual-consistency-vs-strong-consistency-making-right-choice-microservices
Chavan, A. (2022). Importance of identifying and establishing context boundaries while migrating from monolith to microservices. Journal of Engineering and Applied Sciences Technology, 4, E168. http://doi.org/10.47363/JEAST/2022(4)E168
Di Filippo, F. (2023). Defining and Enforcing Data Quality in Data Mesh: a declarative language and execution framework.
Fiorello, N. (2021). A Cloud-based Business Process Automation Platform for Customer Interaction: Research, development, integration, deployment and test of a Business Process Automation platform to manage company customer relations through the cloud.
Georgoulas, P. (2021). Governance Risk and Compliance with the use of Robotic Process Automation & Business Process Management: A path to Hyperautomation (Master's thesis).
Karwa, K. (2023). AI-powered career coaching: Evaluating feedback tools for design students. Indian Journal of Economics & Business. https://www.ashwinanokha.com/ijeb-v22-4-2023.php
Konneru, N. M. K. (2021). Integrating security into CI/CD pipelines: A DevSecOps approach with SAST, DAST, and SCA tools. International Journal of Science and Research Archive. Retrieved from https://ijsra.net/content/role-notification-scheduling-improving-patient
Kumar, A. (2019). The convergence of predictive analytics in driving business intelligence and enhancing DevOps efficiency. International Journal of Computational Engineering and Management, 6(6), 118-142. Retrieved from https://ijcem.in/wp-content/uploads/THE-CONVERGENCE-OF-PREDICTIVE-ANALYTICS-IN-DRIVING-BUSINESS-INTELLIGENCE-AND-ENHANCING-DEVOPS-EFFICIENCY.pdf
LaRock, T., & van de Laar, E. (2023). CPU-Related Wait Types. In Pro SQL Server 2022 Wait Statistics: A Practical Guide to Analyzing Performance in SQL Server and Azure SQL Database (pp. 107-140). Berkeley, CA: Apress.
Lee, W., Kang, M., & Kim, S. (2023). Highly VM-scalable SSD in cloud storage systems. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 43(1), 113-126.
Molnár, B., Pisoni, G., Kherbouche, M., & Zghal, Y. (2023). Blockchain-based business process management (BPM) for finance: the case of credit and claim requests. Smart Cities, 6(3), 1254-1278.
Nascimben, D. (2020). Flexible pathway orchestration engine for healthcare using BPMN and workflow systems.
Nyati, S. (2018). Transforming telematics in fleet management: Innovations in asset tracking, efficiency, and communication. International Journal of Science and Research (IJSR), 7(10), 1804-1810. Retrieved from https://www.ijsr.net/getabstract.php?paperid=SR24203184230
Onyshkiv, R. (2023). Business process management system for optimisation of unstructured businesses.
Panwar, G., Vishwanathan, R., Misra, S., & Bos, A. (2019, November). Sampl: Scalable auditability of monitoring processes using public ledgers. In Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security (pp. 2249-2266).
Raju, R. K. (2017). Dynamic memory inference network for natural language inference. International Journal of Science and Research (IJSR), 6(2). https://www.ijsr.net/archive/v6i2/SR24926091431.pdf
Rameder, H., Di Angelo, M., & Salzer, G. (2022). Review of automated vulnerability analysis of smart contracts on ethereum. Frontiers in Blockchain, 5, 814977.
Sahal, R., Breslin, J. G., & Ali, M. I. (2020). Big data and stream processing platforms for Industry 4.0 requirements mapping for a predictive maintenance use case. Journal of manufacturing systems, 54, 138-151.
Singh, V. (2021). Generative AI in medical diagnostics: Utilizing generative models to create synthetic medical data for training diagnostic algorithms. International Journal of Computer Engineering and Medical Technologies. https://ijcem.in/wp-content/uploads/GENERATIVE-AI-IN-MEDICAL-DIAGNOSTICS-UTILIZING-GENERATIVE-MODELS-TO-CREATE-SYNTHETIC-MEDICAL-DATA-FOR-TRAINING-DIAGNOSTIC-ALGORITHMS.pdf
Suarez, E., Eicker, N., & Lippert, T. (2019). Modular supercomputing architecture: from idea to production. In Contemporary high performance computing (pp. 223-255). CRC Press.
Subramanian, H., & Raj, P. (2019). Hands-On RESTful API Design Patterns and Best Practices: Design, develop, and deploy highly adaptable, scalable, and secure RESTful web APIs. Packt Publishing Ltd.
Wang, C., Ma, H., Liu, S., Li, Y., Ruan, Z., Nguyen, K., ... & Xu, G. H. (2020). Semeru: A {Memory-Disaggregated} managed runtime. In 14th USENIX Symposium on Operating Systems Design and Implementation (OSDI 20) (pp. 261-280).
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Zahir Sayyed
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors retain the copyright of their articles published in this journal. All articles are licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0). This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly cited.