T2I-RISE: Text-to-Insights with Reinforcement learning, Integration of Semantic layers and Enrichment - A Comprehensive Approach with Conversational Context and Feedback Systems

This study introduces T2I-RISE, an innovative approach aimed at enabling natural language querying of databases, which could transform data-driven decision making. Currently, database insights are mostly generated by a limited group of experts. Business Intelligence tools provide some insights, but...

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Vydáno v:IEEE International Conference on Big Data s. 1993 - 1998
Hlavní autoři: Katikeri, Raghu, Phaniraja, Sai, Pratheek, Sai, Desai, Rajvi, Vaid, Amit, Shukla, Neelesh, Jain, Sandeep
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 15.12.2024
Témata:
Business intelligence
Decision making
Evaluation Framework
Insights Generation
Large language models
LLM
Natural languages
Optimization
Reinforcement learning
Semantics
Structured Query Language
Text2SQL
Transforms
ISSN:2573-2978
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Shrnutí:This study introduces T2I-RISE, an innovative approach aimed at enabling natural language querying of databases, which could transform data-driven decision making. Currently, database insights are mostly generated by a limited group of experts. Business Intelligence tools provide some insights, but these are often static. We propose a comprehensive solution tailored for enterprise applications to bridge the gap between user needs and existing capabilities. Despite the industry's increasing use of Text2SQL powered by Large Language Models (LLM), implementation challenges remain, particularly in managing large-scale database schema representations. Our study explores the critical elements needed for user intent comprehension and the generation of corresponding SQL and insights. T2I-RISE offers a comprehensive framework and thorough analysis of these components, including our unique optimization strategies.
ISSN:2573-2978
DOI:10.1109/BigData62323.2024.10825844