DataLab: A Unified Platform for LLM-Powered Business Intelligence
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , , , , , , , , , , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2024
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866910904831967232 |
|---|---|
| author | Weng, Luoxuan Tang, Yinghao Feng, Yingchaojie Chang, Zhuo Chen, Ruiqin Feng, Haozhe Hou, Chen Huang, Danqing Li, Yang Rao, Huaming Wang, Haonan Wei, Canshi Yang, Xiaofeng Zhang, Yuhui Zheng, Yifeng Huang, Xiuqi Zhu, Minfeng Ma, Yuxin Cui, Bin Chen, Peng Chen, Wei |
| author_facet | Weng, Luoxuan Tang, Yinghao Feng, Yingchaojie Chang, Zhuo Chen, Ruiqin Feng, Haozhe Hou, Chen Huang, Danqing Li, Yang Rao, Huaming Wang, Haonan Wei, Canshi Yang, Xiaofeng Zhang, Yuhui Zheng, Yifeng Huang, Xiuqi Zhu, Minfeng Ma, Yuxin Cui, Bin Chen, Peng Chen, Wei |
| contents | Business intelligence (BI) transforms large volumes of data within modern organizations into actionable insights for informed decision-making. Recently, large language model (LLM)-based agents have streamlined the BI workflow by automatically performing task planning, reasoning, and actions in executable environments based on natural language (NL) queries. However, existing approaches primarily focus on individual BI tasks such as NL2SQL and NL2VIS. The fragmentation of tasks across different data roles and tools lead to inefficiencies and potential errors due to the iterative and collaborative nature of BI. In this paper, we introduce DataLab, a unified BI platform that integrates a one-stop LLM-based agent framework with an augmented computational notebook interface. DataLab supports various BI tasks for different data roles in data preparation, analysis, and visualization by seamlessly combining LLM assistance with user customization within a single environment. To achieve this unification, we design a domain knowledge incorporation module tailored for enterprise-specific BI tasks, an inter-agent communication mechanism to facilitate information sharing across the BI workflow, and a cell-based context management strategy to enhance context utilization efficiency in BI notebooks. Extensive experiments demonstrate that DataLab achieves state-of-the-art performance on various BI tasks across popular research benchmarks. Moreover, DataLab maintains high effectiveness and efficiency on real-world datasets from Tencent, achieving up to a 58.58% increase in accuracy and a 61.65% reduction in token cost on enterprise-specific BI tasks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_02205 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | DataLab: A Unified Platform for LLM-Powered Business Intelligence Weng, Luoxuan Tang, Yinghao Feng, Yingchaojie Chang, Zhuo Chen, Ruiqin Feng, Haozhe Hou, Chen Huang, Danqing Li, Yang Rao, Huaming Wang, Haonan Wei, Canshi Yang, Xiaofeng Zhang, Yuhui Zheng, Yifeng Huang, Xiuqi Zhu, Minfeng Ma, Yuxin Cui, Bin Chen, Peng Chen, Wei Databases Artificial Intelligence Computation and Language Business intelligence (BI) transforms large volumes of data within modern organizations into actionable insights for informed decision-making. Recently, large language model (LLM)-based agents have streamlined the BI workflow by automatically performing task planning, reasoning, and actions in executable environments based on natural language (NL) queries. However, existing approaches primarily focus on individual BI tasks such as NL2SQL and NL2VIS. The fragmentation of tasks across different data roles and tools lead to inefficiencies and potential errors due to the iterative and collaborative nature of BI. In this paper, we introduce DataLab, a unified BI platform that integrates a one-stop LLM-based agent framework with an augmented computational notebook interface. DataLab supports various BI tasks for different data roles in data preparation, analysis, and visualization by seamlessly combining LLM assistance with user customization within a single environment. To achieve this unification, we design a domain knowledge incorporation module tailored for enterprise-specific BI tasks, an inter-agent communication mechanism to facilitate information sharing across the BI workflow, and a cell-based context management strategy to enhance context utilization efficiency in BI notebooks. Extensive experiments demonstrate that DataLab achieves state-of-the-art performance on various BI tasks across popular research benchmarks. Moreover, DataLab maintains high effectiveness and efficiency on real-world datasets from Tencent, achieving up to a 58.58% increase in accuracy and a 61.65% reduction in token cost on enterprise-specific BI tasks. |
| title | DataLab: A Unified Platform for LLM-Powered Business Intelligence |
| topic | Databases Artificial Intelligence Computation and Language |
| url | https://arxiv.org/abs/2412.02205 |