SiriusBI: A Comprehensive LLM-Powered Solution for Data Analytics in Business Intelligence
Jie Jiang, Haining Xie, Siqi Shen, Yu Shen, Zihan Zhang, Meng Lei, Yifeng Zheng, Yang Li, Chunyou Li, Danqing Huang, Yinjun Wu, Wentao Zhang, Xiaofeng Yang, Bin Cui, Peng Chen

TL;DR
SiriusBI is an innovative LLM-powered business intelligence system that enhances functionality, interaction, and cross-domain adaptability, significantly improving SQL accuracy and reducing query time in real-world enterprise settings.
Contribution
The paper introduces SiriusBI, a comprehensive BI system with multi-module architecture, multi-round dialogue, and data-conditioned SQL generation, addressing key industrial deployment challenges of LLM-based BI solutions.
Findings
Achieves over 93% SQL generation accuracy.
Reduces data analysts' query time from minutes to seconds.
Demonstrates effectiveness on real-world datasets and benchmarks.
Abstract
With the proliferation of Large Language Models (LLMs) in Business Intelligence (BI), existing solutions face critical challenges in industrial deployments: functionality deficiencies from legacy systems failing to meet evolving LLM-era user demands, interaction limitations from single-round SQL generation paradigms inadequate for multi-round clarification, and cost for domain adaptation arising from cross-domain methods migration. We present SiriusBI, a practical LLM-powered BI system addressing the challenges of industrial deployments through three key innovations: (a) An end-to-end architecture integrating multi-module coordination to overcome functionality gaps in legacy systems; (b) A multi-round dialogue with querying mechanism, consisting of semantic completion, knowledge-guided clarification, and proactive querying processes, to resolve interaction constraints in SQL…
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Taxonomy
TopicsBig Data and Business Intelligence
