LLM-Driven Online Aggregation for Unstructured Text Analytics
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866910046432002048 |
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| author | Hui, Chao Lu, Weizheng Gao, Yanjie Xiong, Lingfeng Wang, Yunhai Chen, Yueguo |
| author_facet | Hui, Chao Lu, Weizheng Gao, Yanjie Xiong, Lingfeng Wang, Yunhai Chen, Yueguo |
| contents | Large Language Models (LLMs) exhibit strong capabilities in text processing, and recent research has augmented SQL and DataFrame with LLM-powered semantic operators for data analysis. However, LLM-based data processing is hindered by slower token generation speeds compared to relational queries. To enhance real-time responsiveness, we propose OLLA, an LLM-driven online aggregation framework that accelerates semantic processing within relational queries. In contrast to batch-processing systems that yield results only after the entire dataset is processed, our approach incrementally transforms text into a structured data stream and applies online aggregation to provide progressive output. To enhance our online aggregation process, we introduce a semantic stratified sampling approach that improves data selection and expedites convergence to the ground truth. Evaluations show that OLLA reaches 1% accuracy error bound compared with labeled ground truth using less than 4% of the full-data time. It achieves speedups ranging from 1.6$\times$ to 38$\times$ across diverse domains, measured by comparing the time to reach a 5% error bound with that of full-data time. We release our code at https://github.com/olla-project/llm-online-agg.git. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_08443 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | LLM-Driven Online Aggregation for Unstructured Text Analytics Hui, Chao Lu, Weizheng Gao, Yanjie Xiong, Lingfeng Wang, Yunhai Chen, Yueguo Databases Large Language Models (LLMs) exhibit strong capabilities in text processing, and recent research has augmented SQL and DataFrame with LLM-powered semantic operators for data analysis. However, LLM-based data processing is hindered by slower token generation speeds compared to relational queries. To enhance real-time responsiveness, we propose OLLA, an LLM-driven online aggregation framework that accelerates semantic processing within relational queries. In contrast to batch-processing systems that yield results only after the entire dataset is processed, our approach incrementally transforms text into a structured data stream and applies online aggregation to provide progressive output. To enhance our online aggregation process, we introduce a semantic stratified sampling approach that improves data selection and expedites convergence to the ground truth. Evaluations show that OLLA reaches 1% accuracy error bound compared with labeled ground truth using less than 4% of the full-data time. It achieves speedups ranging from 1.6$\times$ to 38$\times$ across diverse domains, measured by comparing the time to reach a 5% error bound with that of full-data time. We release our code at https://github.com/olla-project/llm-online-agg.git. |
| title | LLM-Driven Online Aggregation for Unstructured Text Analytics |
| topic | Databases |
| url | https://arxiv.org/abs/2603.08443 |