Natural Language Querying in Oracle Fusion Analytics: A Step toward Conversational BI
DOI:
https://doi.org/10.63282/3050-9246.IJETCSIT-V3I3P109Keywords:
Oracle Fusion Analytics, Natural Language Processing, Conversational BI, Voice Assistants, Business IntelligenceAbstract
Natural Language Processing (NLP) has emerged as a transformative technology in Business Intelligence (BI), enabling business users to query complex data systems without requiring specialised technical knowledge. Oracle Fusion Analytics is a modern cloud-based analytics suite that integrates seamlessly with Oracle Cloud Applications, offering deep insights across domains like finance, HR, supply chain, and customer experience. By integrating natural language querying (NLQ) capabilities into Oracle Fusion Analytics, enterprises can significantly enhance user accessibility, data democratization, and decision-making speed. This paper provides a comprehensive study of the integration of NLP interfaces in Oracle Fusion Analytics with a focus on Conversational BI. We present an in-depth analysis of how natural language interfaces, including text-based and voice-based assistants, empower users to generate self-service reports and obtain real-time answers to business questions. Furthermore, we examine the technological foundations of NLP in analytics, explore existing literature, propose a methodology for implementing NLQ in Oracle Fusion Analytics, and discuss experimental results. Key challenges such as language ambiguity, user intent detection, and performance optimization are addressed. This paper aims to contribute to the growing body of research by presenting a structured approach, supported with figures, tables, flowcharts, and real-world application scenarios, highlighting the future potential of conversational BI
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References
[1] Androutsopoulos, I., Ritchie, G. D., & Thanisch, P. (1995). Natural language interfaces to databases–an introduction. Natural language engineering, 1(1), 29-81.
[2] Affolter, K., Stockinger, K., & Bernstein, A. (2019). A comparative survey of recent natural language interfaces for databases. The VLDB Journal, 28(5), 793-819.
[3] Gao, J., Galley, M., & Li, L. (2018, June). Neural approaches to conversational AI. In The 41st international ACM SIGIR conference on research & development in information retrieval (pp. 1371-1374).
[4] Zhang, Z., Takanobu, R., Zhu, Q., Huang, M., & Zhu, X. (2020). Recent Advances and Challenges in Task-Oriented Dialogue Systems. Science China Technological Sciences, 63(10), 2011-2027.
[5] Hoy, M. B. (2018). Alexa, Siri, Cortana, and more: an introduction to voice assistants. Medical Reference Services Quarterly, 37(1), 81-88.
[6] Tavera Romero, C. A., Ortiz, J. H., Khalaf, O. I., & Ríos Prado, A. (2021). Business intelligence: business evolution after Industry 4.0. Sustainability, 13(18), 10026.
[7] Panian, Z. (2012, July). The evolution of business intelligence: from historical data mining to mobile and location-based intelligence. In WSEAS International Conference on Recent Research in Business and Economics, ISBN (pp. 971-978).
[8] Mikroyannidis, A., & Theodoulidis, B. (2010). Ontology management and evolution for business intelligence. International journal of information management, 30(6), 559-566.
[9] Safwan, E. R., Meredith, R., & Burstein, F. (2016). Business Intelligence (BI) system evolution: a case in a healthcare institution. Journal of Decision Systems, 25(sup1), 463-475.
[10] Quamar, A., Özcan, F., Miller, D., Moore, R. J., Niehus, R., & Kreulen, J. (2020). Conversational BI: An ontology-driven conversation system for business intelligence applications. Proceedings of the VLDB Endowment, 13(12), 3369-3381.
[11] Thakker, T. (2015). Introduction to Oracle Fusion Applications. In Pro Oracle Fusion Applications: Installation and Administration (pp. 3-22). Berkeley, CA: Apress.
[12] Lei, C., Özcan, F., Quamar, A., Mittal, A. R., Sen, J., Saha, D., & Sankaranarayanan, K. (2018). Ontology-Based Natural Language Query Interfaces for Data Exploration. IEEE Data Eng. Bull., 41(3), 52-63.
[13] Graliński, F., Jassem, K., & Junczys-Dowmunt, M. (2012). PSI-toolkit: A natural language processing pipeline. In Computational Linguistics: Applications (pp. 27-39). Berlin, Heidelberg: Springer Berlin Heidelberg.
[14] Molnar, K. K., & Kletke, M. G. (1996). The impacts on user performance and satisfaction of a voice-based front-end interface for a standard software tool. International Journal of Human-Computer Studies, 45(3), 287-303.
[15] Kaufmann, E., & Bernstein, A. (2010). Evaluating the usability of natural language query languages and interfaces to semantic web knowledge bases. Journal of Web Semantics, 8(4), 377-393.
[16] Xu, B., Cai, R., Zhang, Z., Yang, X., Hao, Z., Li, Z., & Liang, Z. (2019). NADAQ: natural language database querying based on deep learning. IEEE Access, 7, 35012-35017.
[17] Yu, D., & Deng, L. (2016). Automatic speech recognition (Vol. 1). Berlin: Springer.
[18] Halper, F. (2017). Advanced analytics: Moving toward AI, machine learning, and natural language processing. TDWI Best Practices Report.
[19] Nethravathi, B., Amitha, G., Saruka, A., Bharath, T. P., & Suyagya, S. (2020). Structuring natural language to query language: a review. Engineering, Technology & Applied Science Research, 10(6), 6521-6525.
[20] Jamil, H. M. (2017, August). Knowledge-rich natural language queries over structured biological databases. In Proceedings of the 8th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics (pp. 352-361).
[21] Pappula, K. K. (2020). Browser-Based Parametric Modeling: Bridging Web Technologies with CAD Kernels. International Journal of Emerging Trends in Computer Science and Information Technology, 1(3), 56-67. https://doi.org/10.63282/3050-9246.IJETCSIT-V1I3P107
[22] Rahul, N. (2020). Vehicle and Property Loss Assessment with AI: Automating Damage Estimations in Claims. International Journal of Emerging Research in Engineering and Technology, 1(4), 38-46. https://doi.org/10.63282/3050-922X.IJERET-V1I4P105
[23] Enjam, G. R., & Chandragowda, S. C. (2020). Role-Based Access and Encryption in Multi-Tenant Insurance Architectures. International Journal of Emerging Trends in Computer Science and Information Technology, 1(4), 58-66. https://doi.org/10.63282/3050-9246.IJETCSIT-V1I4P107
[24] Pappula, K. K. (2021). Modern CI/CD in Full-Stack Environments: Lessons from Source Control Migrations. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 2(4), 51-59. https://doi.org/10.63282/3050-9262.IJAIDSML-V2I4P106
[25] Rahul, N. (2021). AI-Enhanced API Integrations: Advancing Guidewire Ecosystems with Real-Time Data. International Journal of Emerging Research in Engineering and Technology, 2(1), 57-66. https://doi.org/10.63282/3050-922X.IJERET-V2I1P107
[26] Enjam, G. R., Chandragowda, S. C., & Tekale, K. M. (2021). Loss Ratio Optimization using Data-Driven Portfolio Segmentation. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 2(1), 54-62. https://doi.org/10.63282/3050-9262.IJAIDSML-V2I1P107
