Strategies for Data Lakes in Financial Data Management
DOI:
https://doi.org/10.32628/CSEIT2410612413Keywords:
Data lakes, financial data management, big data, data governance, data analyticsAbstract
The deployment and optimization of data lakes in financial data management is investigated in this research article. Concerning an ever-growing volume and diversity of data, conventional data management technologies are showing insufficient capability for financial organizations. Providing a scalable and flexible infrastructure for storing and evaluating enormous volumes of organized and unstructured data, data lakes provide a good answer. With an eye on data governance, security, and analytics, this paper investigates many approaches for building, running, and managing data lakes in the financial sector. By means of an extensive literature study and case studies, we pinpoint areas of best practices and difficulties in implementing data lake solutions for financial institutions. The work ends with suggestions for further studies and useful field applications.
Downloads
References
Alrehamy, H., & Walker, C. (2015). Personal data lake with data gravity pull. In 2015 IEEE Fifth International Conference on Big Data and Cloud Computing (pp. 160-167). IEEE.
Cheng, Y., Zhao, S., Yu, B., Chen, H., & Wu, Y. (2019). A survey of data lake architecture and key technologies. In 2019 IEEE 21st International Conference on High Performance Computing and Communications; IEEE 17th International Conference on Smart City; IEEE 5th International Conference on Data Science and Systems (HPCC/SmartCity/DSS) (pp. 2953-2960). IEEE.
Deloitte. (2019). The data-driven enterprise: How to use data to drive business value. Deloitte Insights.
Dixon, J. (2010). Pentaho, Hadoop, and Data Lakes. James Dixon's Blog.
Gartner. (2018). Gartner Survey Finds 91 Percent of Organizations Have Not Reached a "Transformational" Level of Maturity in Data and Analytics. Gartner Press Release.
Gübeli, T., Leymann, F., & Wieland, M. (2021). Data Lakes in the Financial Services Industry: Challenges and Opportunities. In Business Information Systems (pp. 235-246). Springer.
Khine, P. P., & Wang, Z. S. (2018). Data lake: a new ideology in big data era. ITM Web of Conferences, 17, 03025. DOI: https://doi.org/10.1051/itmconf/20181703025
Miloslavskaya, N., & Tolstoy, A. (2016). Big data, fast data and data lake concepts. Procedia Computer Science, 88, 300-305. DOI: https://doi.org/10.1016/j.procs.2016.07.439
NewVantage Partners. (2021). Big Data and AI Executive Survey 2021: Executive Summary of Findings. NewVantage Partners LLC.
Pasupuleti, P., & Purra, B. S. (2015). Data Lake Development with Big Data. Packt Publishing Ltd.
Singh, A., & Sharma, S. (2020). Fraud detection in financial services using machine learning and data lakes. International Journal of Innovative Technology and Exploring Engineering, 9(5), 1378-1383.
Gartner. (2020). Gartner Top 10 Data and Analytics Trends for 2021. Gartner Research.
IBM. (2019). The Future of Financial Services: How Artificial Intelligence is Transforming the Financial Services Industry. IBM Institute for Business Value.
Informatica. (2021). The Definitive Guide to Data Governance for Financial Services. Informatica White Paper.
McKinsey & Company. (2018). The data-driven enterprise of 2025. McKinsey Digital.
O'Reilly, J. (2020). Data Lakes for Dummies. John Wiley & Sons.
PwC. (2020). Financial Services Technology 2020 and Beyond: Embracing disruption. PwC Global.
Russom, P. (2017). Data Lakes: Purposes, Practices, Patterns, and Platforms. TDWI Research.
Sawant, N., & Shah, H. (2021). Big Data Application Architecture Q&A: A Problem-Solution Approach. Apress.
Tallon, P. P. (2013). Corporate governance of big data: Perspectives on value, risk, and cost. Computer, 46(6), 32-38. DOI: https://doi.org/10.1109/MC.2013.155
Terrizzano, I. G., Schwarz, P. M., Roth, M., & Colino, J. E. (2015). Data Wrangling: The Challenging Journey from the Wild to the Lake. In CIDR.
Wixom, B. H., & Ross, J. W. (2017). How to monetize your data. MIT Sloan Management Review, 58(3), 10-13.
Zhu, Y., & Xiong, Y. (2015). Dataology and data science: Up to now. Pattern Recognition and Artificial Intelligence, 28(9), 785-801.
Azarmi, B. (2016). Scalable Big Data Architecture: A practitioners guide to choosing relevant Big Data architecture. Apress. DOI: https://doi.org/10.1007/978-1-4842-1326-1
Baesens, B., Bapna, R., Marsden, J. R., Vanthienen, J., & Zhao, J. L. (2016). Transformational issues of big data and analytics in networked business. MIS Quarterly, 40(4), 807-818. DOI: https://doi.org/10.25300/MISQ/2016/40:4.03
Chen, H. M., Kazman, R., & Matthes, F. (2015). Demystifying big data adoption: Beyond IT fashion and relative advantage. In PACIS (p. 240).
Davenport, T. H., & Bean, R. (2018). Big companies are embracing analytics, but most still don't have a data-driven culture. Harvard Business Review Digital Articles, 2-4.
Duan, Y., Edwards, J. S., & Dwivedi, Y. K. (2019). Artificial intelligence for decision making in the era of Big Data–evolution, challenges and research agenda. International Journal of Information Management, 48, 63-71. DOI: https://doi.org/10.1016/j.ijinfomgt.2019.01.021
Fang, H. (2015). Managing data lakes in big data era: What's a data lake and why has it became popular in data management ecosystem. In 2015 IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems (CYBER) (pp. 820-824). IEEE. DOI: https://doi.org/10.1109/CYBER.2015.7288049
Günther, W. A., Rezazade Mehrizi, M. H., Huysman, M., & Feldberg, F. (2017). Debating big data: A literature review on realizing value from big data. The Journal of Strategic Information Systems, 26(3), 191-209. DOI: https://doi.org/10.1016/j.jsis.2017.07.003
Hagstrom, M. (2019). Architecting a Data Lake for Financial Services. O'Reilly Media, Inc.
Khatri, V., & Brown, C. V. (2010). Designing data governance. Communications of the ACM, 53(1), 148-152. DOI: https://doi.org/10.1145/1629175.1629210
LaValle, S., Lesser, E., Shockley, R., Hopkins, M. S., & Kruschwitz, N. (2011). Big data, analytics and the path from insights to value. MIT Sloan Management Review, 52(2), 21-32.
Madera, C., & Laurent, A. (2016). The next information architecture evolution: The data lake wave. In Proceedings of the 8th International Conference on Management of Digital EcoSystems (pp. 174-180). DOI: https://doi.org/10.1145/3012071.3012077
Marr, B. (2017). Data strategy: How to profit from a world of big data, analytics and the internet of things. Kogan Page Publishers.
Mikalef, P., Pappas, I. O., Krogstie, J., & Giannakos, M. (2018). Big data analytics capabilities: a systematic literature review and research agenda. Information Systems and e-Business Management, 16(3), 547-578. DOI: https://doi.org/10.1007/s10257-017-0362-y
Nadkarni, A., & Vesset, D. (2020). Worldwide Big Data and Analytics Software Market Shares, 2019: Investment in Data Continues. IDC Market Share.
Ramakrishnan, R., Sridharan, B., Douceur, J. R., Kasturi, P., Krishnamachari-Sampath, B., Krishnamoorthy, K., ... & Zhao, Y. (2017). Azure data lake store: a hyperscale distributed file service for big data analytics. In Proceedings of the 2017 ACM International Conference on Management of Data (pp. 51-63). DOI: https://doi.org/10.1145/3035918.3056100
Ransbotham, S., & Kiron, D. (2017). Analytics as a source of business innovation. MIT Sloan Management Review, 58(3).
Saggi, M. K., & Jain, S. (2018). A survey towards an integration of big data analytics to big insights for value-creation. Information Processing & Management, 54(5), 758-790. DOI: https://doi.org/10.1016/j.ipm.2018.01.010
Sivarajah, U., Kamal, M. M., Irani, Z., & Weerakkody, V. (2017). Critical analysis of Big Data challenges and analytical methods. Journal of Business Research, 70, 263-286. DOI: https://doi.org/10.1016/j.jbusres.2016.08.001
Stein, B., & Morrison, A. (2014). The enterprise data lake: Better integration and deeper analytics. PwC Technology Forecast: Rethinking integration, 1(1), 1-9.
Tamburri, D. A. (2020). Design principles for the general data protection regulation (GDPR): A formal concept analysis and its evaluation. Information Systems, 91, 101469. DOI: https://doi.org/10.1016/j.is.2019.101469
Vial, G. (2019). Understanding digital transformation: A review and a research agenda. The Journal of Strategic Information Systems, 28(2), 118-144. DOI: https://doi.org/10.1016/j.jsis.2019.01.003
Wang, L., & Alexander, C. A. (2015). Big data driven supply chain management and business administration. American Journal of Economics and Business Administration, 7(2), 60-67. DOI: https://doi.org/10.3844/ajebasp.2015.60.67
Watson, H. J. (2019). Update Tutorial: Big Data Analytics: Concepts, Technology, and Applications. Communications of the Association for Information Systems, 44(1), 21.
Ylijoki, O., & Porras, J. (2016). Perspectives to definition of big data: a mapping study and discussion. Journal of Innovation Management, 4(1), 69-91. DOI: https://doi.org/10.24840/2183-0606_004.001_0006
Naveen Bagam, International Journal of Computer Science and Mobile Computing, Vol.13 Issue.11, November- 2024, pg. 6-27 DOI: https://doi.org/10.47760/ijcsmc.2024.v13i11.002
Naveen Bagam. (2024). Optimization of Data Engineering Processes Using AI. International Journal of Research Radicals in Multidisciplinary Fields, ISSN: 2960-043X, 3(1), 20–34. Retrieved from https://www.researchradicals.com/index.php/rr/article/view/138
Naveen Bagam. (2024). Machine Learning Models for Customer Segmentation in Telecom. Journal of Sustainable Solutions, 1(4), 101–115. https://doi.org/10.36676/j.sust.sol.v1.i4.42 DOI: https://doi.org/10.36676/j.sust.sol.v1.i4.42
Bagam, N. (2023). Implementing Scalable Data Architecture for Financial Institutions. Stallion Journal for Multidisciplinary Associated Research Studies, 2(3), 27
Bagam, N. (2021). Advanced Techniques in Predictive Analytics for Financial Services. Integrated Journal for Research in Arts and Humanities, 1(1), 117–126. https://doi.org/10.55544/ijrah.1.1.16 DOI: https://doi.org/10.55544/ijrah.1.1.16
Enhancing Data Pipeline Efficiency in Large-Scale Data Engineering Projects. (2019). International Journal of Open Publication and Exploration, ISSN: 3006-2853, 7(2), 44- Sai Krishna Shiramshetty. (2024). Enhancing SQL Performance for Real-Time Business Intelligence Applications. International Journal of Multidisciplinary Innovation and Research Methodology, ISSN: 2960-2068, 3(3), 282–297. Retrieved from https://ijmirm.com/index.php/ijmirm/article/view/138
Sai Krishna Shiramshetty, "Big Data Analytics in Civil Engineering : Use Cases and Techniques", International Journal of Scientific Research in Civil Engineering (IJSRCE), ISSN : 2456-6667, Volume 3, Issue 1, pp.39-46, January-February.2019 DOI: https://doi.org/10.32628/IJSRCE19318
URL : https://ijsrce.com/IJSRCE19318
Sai Krishna Shiramshetty, " Data Integration Techniques for Cross-Platform Analytics, IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 6, Issue 4, pp.593-599, July-August-2020. Available at doi : https://doi.org/10.32628/CSEIT2064139 DOI: https://doi.org/10.32628/CSEIT2064139
Shiramshetty, S. K. (2021). SQL BI Optimization Strategies in Finance and Banking. Integrated Journal for Research in Arts and Humanities, 1(1), 106–116. https://doi.org/10.55544/ijrah.1.1.15 DOI: https://doi.org/10.55544/ijrah.1.1.15
Sai Krishna Shiramshetty. (2022). Predictive Analytics Using SQL for Operations Management. Eduzone: International Peer Reviewed/Refereed Multidisciplinary Journal, 11(2), 433–448. Retrieved from https://eduzonejournal.com/index.php/eiprmj/article/view/693
Shiramshetty, S. K. (2023). Data warehousing solutions for business intelligence. International Journal of Computer Science and Mobile Computing, 12(3), 49–62. https://ijcsmc.com/index.php/volume-12-issue-3-march-2023/ DOI: https://doi.org/10.47760/ijcsmc.2023.v12i03.006
Sai Krishna Shiramshetty. (2024). Comparative Study of BI Tools for Real-Time Analytics. International Journal of Research and Review Techniques, 3(3), 1–13. Retrieved from https://ijrrt.com/index.php/ijrrt/article/view/210
Sai Krishna Shiramshetty "Leveraging BI Development for Decision-Making in Large Enterprises" Iconic Research And Engineering Journals Volume 8 Issue 5 2024 Page 548-560
Sai Krishna Shiramshetty "Integrating SQL with Machine Learning for Predictive Insights" Iconic Research And Engineering Journals Volume 1 Issue 10 2018 Page 287-292
Shiramshetty, S. K. (2023). Advanced SQL Query Techniques for Data Analysis in Healthcare. Journal for Research in Applied Sciences and Biotechnology, 2(4), 248–258. https://doi.org/10.55544/jrasb.2.4.33 DOI: https://doi.org/10.55544/jrasb.2.4.33
https://ijope.com/index.php/home/article/view/166
Kola, H. G. (2024). Optimizing ETL Processes for Big Data Applications. International Journal of Engineering and Management Research, 14(5), 99–112. https://doi.org/10.5281/zenodo.14184235
SQL in Data Engineering: Techniques for Large Datasets. (2023). International Journal of Open Publication and Exploration, ISSN: 3006-2853, 11(2), 36-51. https://ijope.com/index.php/home/article/view/165
Data Integration Strategies in Cloud-Based ETL Systems. (2023). International Journal of Transcontinental Discoveries, ISSN: 3006-628X, 10(1), 48-62. https://internationaljournals.org/index.php/ijtd/article/view/116
Harish Goud Kola. (2024). Real-Time Data Engineering in the Financial Sector. International Journal of Multidisciplinary Innovation and Research Methodology, ISSN: 2960-2068, 3(3), 382–396. Retrieved from https://ijmirm.com/index.php/ijmirm/article/view/143
Harish Goud Kola. (2022). Best Practices for Data Transformation in Healthcare ETL. Edu Journal of International Affairs and Research, ISSN: 2583-9993, 1(1), 57–73. Retrieved from https://edupublications.com/index.php/ejiar/article/view/106
Kola, H. G. (2018). Data warehousing solutions for scalable ETL pipelines. International Journal of Scientific Research in Science, Engineering and Technology, 4(8), 762. https://doi.org/10.1.1.123.4567
Harish Goud Kola, " Building Robust ETL Systems for Data Analytics in Telecom , IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 5, Issue 3, pp.694-700, May-June-2019. Available at doi : https://doi.org/10.32628/CSEIT1952292
Kola, H. G. (2022). Data security in ETL processes for financial applications. International Journal of Enhanced Research in Science, Technology & Engineering, 11(9), 55. https://ijsrcseit.com/CSEIT1952292.
Santhosh Bussa, "Advancements in Automated ETL Testing for Financial Applications", IJRAR - International Journal of Research and Analytical Reviews (IJRAR), E-ISSN 2348-1269, P- ISSN 2349-5138, Volume.7, Issue 4, Page No pp.426-443, November 2020, Available at : http://www.ijrar.org/IJRAR2AA1744.pdf
Bussa, S. (2023). Artificial Intelligence in Quality Assurance for Software Systems. Stallion Journal for Multidisciplinary Associated Research Studies, 2(2), 15–26. https://doi.org/10.55544/sjmars.2.2.2.
Bussa, S. (2021). Challenges and solutions in optimizing data pipelines. International Journal for Innovative Engineering and Management Research, 10(12), 325–341. https://sjmars.com/index.php/sjmars/article/view/116
Bussa, S. (2022). Machine Learning in Predictive Quality Assurance. Stallion Journal for Multidisciplinary Associated Research Studies, 1(6), 54–66. https://doi.org/10.55544/sjmars.1.6.8
Bussa, S. (2022). Emerging trends in QA testing for AI-driven software. International Journal of All Research Education and Scientific Methods (IJARESM, 10(11), 1712. Retrieved from http://www.ijaresm.com
Santhosh Bussa. (2024). Evolution of Data Engineering in Modern Software Development. Journal of Sustainable Solutions, 1(4), 116–130. https://doi.org/10.36676/j.sust.sol.v1.i4.43 DOI: https://doi.org/10.36676/j.sust.sol.v1.i4.43
Santhosh Bussa. (2024). Big Data Analytics in Financial Systems Testing. International Journal of Multidisciplinary Innovation and Research Methodology, ISSN: 2960-2068, 3(3), 506–521. Retrieved from https://ijmirm.com/index.php/ijmirm/article/view/150
Bussa, S. (2019). AI-driven test automation frameworks. International Journal for Innovative Engineering and Management Research, 8(10), 68–87. Retrieved from https://www.ijiemr.org/public/uploads/paper/427801732865437.pdf
Santhosh Bussa. (2023). Role of Data Science in Improving Software Reliability and Performance. Edu Journal of International Affairs and Research, ISSN: 2583-9993, 2(4), 95–111. Retrieved from https://edupublications.com/index.php/ejiar/article/view/111
Bussa, S. (2023). Enhancing BI tools for improved data visualization and insights. International Journal of Computer Science and Mobile Computing, 12(2), 70–92. https://doi.org/10.47760/ijcsmc.2023.v12i02.005 DOI: https://doi.org/10.47760/ijcsmc.2023.v12i02.005
Annam, S. N. (2020). Innovation in IT project management for banking systems. International Journal of Enhanced Research in Science, Technology & Engineering, 9(10), 19. https://www.erpublications.com/uploaded_files/download/sri-nikhil-annam_gBNPz.pdf
Annam, S. N. (2018). Emerging trends in IT management for large corporations. International Journal of Scientific Research in Science, Engineering and Technology, 4(8), 770. https://ijsrset.com/paper/12213.pdf
Sri Nikhil Annam, " IT Leadership Strategies for High-Performance Teams, IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 7, Issue 1, pp.302-317, January-February-2021. Available at doi : https://doi.org/10.32628/CSEIT228127 DOI: https://doi.org/10.32628/CSEIT228127
Annam, S. N. (2024). Comparative Analysis of IT Management Tools in Healthcare. Stallion Journal for Multidisciplinary Associated Research Studies, 3(5), 72–86. https://doi.org/10.55544/sjmars.3.5.9.
Annam, N. (2024). AI-Driven Solutions for IT Resource Management. International Journal of Engineering and Management Research, 14(6), 15–30. https://doi.org/10.31033/ijemr.14.6.15-30 DOI: https://doi.org/10.31033/ijemr.14.6.15-30
Annam, S. N. (2022). Optimizing IT Infrastructure for Business Continuity. Stallion Journal for Multidisciplinary Associated Research Studies, 1(5), 31–42. https://doi.org/10.55544/sjmars.1.5.7
Sri Nikhil Annam , " Managing IT Operations in a Remote Work Environment, IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 8, Issue 5, pp.353-368, September-October-2022. https://ijsrcseit.com/paper/CSEIT23902179.pdf
Annam, S. (2023). Data security protocols in telecommunication systems. International Journal for Innovative Engineering and Management Research, 8(10), 88–106. https://www.ijiemr.org/downloads/paper/Volume-8/data-security-protocols-in-telecommunication-systems
Annam, S. N. (2023). Enhancing IT support for enterprise-scale applications. International Journal of Enhanced Research in Science, Technology & Engineering, 12(3), 205. https://www.erpublications.com/uploaded_files/download/sri-nikhil-annam_urfNc.pdf
Kola, H. G. (2024). Optimizing ETL Processes for Big Data Applications. International Journal of Engineering and Management Research, 14(5), 99–112. https://doi.org/10.5281/zenodo.14184235
SQL in Data Engineering: Techniques for Large Datasets. (2023). International Journal of Open Publication and Exploration, ISSN: 3006-2853, 11(2), 36-51. https://ijope.com/index.php/home/article/view/165
Data Integration Strategies in Cloud-Based ETL Systems. (2023). International Journal of Transcontinental Discoveries, ISSN: 3006-628X, 10(1), 48-62. https://internationaljournals.org/index.php/ijtd/article/view/116
Harish Goud Kola. (2024). Real-Time Data Engineering in the Financial Sector. International Journal of Multidisciplinary Innovation and Research Methodology, ISSN: 2960-2068, 3(3), 382–396. Retrieved from https://ijmirm.com/index.php/ijmirm/article/view/143
Harish Goud Kola. (2022). Best Practices for Data Transformation in Healthcare ETL. Edu Journal of International Affairs and Research, ISSN: 2583-9993, 1(1), 57–73. Retrieved from https://edupublications.com/index.php/ejiar/article/view/106
Kola, H. G. (2018). Data warehousing solutions for scalable ETL pipelines. International Journal of Scientific Research in Science, Engineering and Technology, 4(8), 762. https://doi.org/10.1.1.123.4567
Harish Goud Kola, " Building Robust ETL Systems for Data Analytics in Telecom , IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 5, Issue 3, pp.694-700, May-June-2019. Available at doi : https://doi.org/10.32628/CSEIT1952292 DOI: https://doi.org/10.32628/CSEIT1952292
Kola, H. G. (2022). Data security in ETL processes for financial applications. International Journal of Enhanced Research in Science, Technology & Engineering, 11(9), 55. https://ijsrcseit.com/CSEIT1952292.
Naveen Bagam. (2024). Data Integration Across Platforms: A Comprehensive Analysis of Techniques, Challenges, and Future Directions. International Journal of Intelligent Systems and Applications in Engineering, 12(23s), 902–919. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/7062
Naveen Bagam, Sai Krishna Shiramshetty, Mouna Mothey, Harish Goud Kola, Sri Nikhil Annam, & Santhosh Bussa. (2024). Advancements in Quality Assurance and Testing in Data Analytics. Journal of Computational Analysis and Applications (JoCAAA), 33(08), 860–878. Retrieved from https://eudoxuspress.com/index.php/pub/article/view/1487
Bagam, N., Shiramshetty, S. K., Mothey, M., Kola, H. G., Annam, S. N., & Bussa, S. (2024). Optimizing SQL for BI in diverse engineering fields. International Journal of Communication Networks and Information Security, 16(5). https://ijcnis.org/
Bagam, N., Shiramshetty, S. K., Mothey, M., Annam, S. N., & Bussa, S. (2024). Machine Learning Applications in Telecom and Banking. Integrated Journal for Research in Arts and Humanities, 4(6), 57–69. https://doi.org/10.55544/ijrah.4.6.8 DOI: https://doi.org/10.55544/ijrah.4.6.8
Bagam, N., Shiramshetty, S. K., Mothey, M., Kola, H. G., Annam, S. N., & Bussa, S. (2024). Collaborative approaches in data engineering and analytics. International Journal of Communication Networks and Information Security, 16(5). https://ijcnis.org/
Kulkarni, A. (2024). Natural Language Processing for Text Analytics in SAP HANA. International Journal of Multidisciplinary Innovation and Research Methodology (IJMIRM), ISSN, 2960-2068. https://scholar.google.com/scholar?oi=bibs&cluster=15918532763612424504&btnI=1&hl=en
Kulkarni, A. (2024). Digital Transformation with SAP Hana. International Journal on Recent and Innovation Trends in Computing and Communication ISSN, 2321-8169. https://scholar.google.com/scholar?cluster=12193741245105822786&hl=en&as_sdt=2005
Kulkarni, A. (2024). Enhancing Customer Experience with AI-Powered Recommendations in SAP HANA. International Journal of Business Management and Visuals, ISSN, 3006-2705. https://scholar.google.com/scholar?cluster=8922856457601624723&hl=en&as_sdt=2005&as_ylo=2024&as_yhi=2024
Kulkarni, A. (2024). Generative AI-Driven for SAP Hana Analytics. International Journal on Recent and Innovation Trends in Computing and Communication, 12(2), 438-444. https://scholar.google.com/scholar?cluster=10311553701865565222&hl=en&as_sdt=2005
S. Dodda, "Exploring Variational Autoencoders and Generative Latent Time-Series Models for Synthetic Data Generation and Forecasting," 2024 Control Instrumentation System Conference (CISCON), Manipal, India, 2024, pp. 1-6, doi: 10.1109/CISCON62171.2024.10696588. DOI: https://doi.org/10.1109/CISCON62171.2024.10696588
S. Dodda, "Enhancing Foreground-Background Segmentation for Indoor Autonomous Navigation using Superpixels and Decision Trees," 2024 Control Instrumentation System Conference (CISCON), Manipal, India, 2024, pp. 1-7, doi: 10.1109/CISCON62171.2024.10696719. DOI: https://doi.org/10.1109/CISCON62171.2024.10696719
Downloads
Published
Issue
Section
License
Copyright (c) 2024 International Journal of Scientific Research in Computer Science, Engineering and Information Technology
This work is licensed under a Creative Commons Attribution 4.0 International License.
