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Relational databases play an important role in business, science, and more. However, many users cannot fully unleash the analytical power of relational databases, because they are not familiar with database languages such as SQL. Many techniques have been proposed to automatically generate SQL from natural language, but they suffer from two issues: (1) they still make many mistakes, particularly for complex queries, and (2) they do not provide a flexible way for non-expert users to validate and refine incorrect queries. To address these issues, we introduce a new interaction mechanism that allows users to directly edit a step-by-step explanation of a query to fix errors. Our experiments on multiple datasets, as well as a user study with 24 participants, demonstrate that our approach can achieve better performance than multiple SOTA approaches.
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ThalamusDB: Approximate Query Processing on Multi-Modal Data
We introduce ThalamusDB, a novel approximate query processing system that processes complex SQL queries on multi-modal data. ThalamusDB supports SQL queries integrating natural language predicates on visual, audio, and text data. To answer such queries, ThalamusDB exploits a collection of zero-shot models in combination with relational processing. ThalamusDB utilizes deterministic approximate query processing, harnessing the relative efficiency of relational processing to mitigate the computational demands of machine learning inference. For evaluating a natural language predicate, ThalamusDB requests a small number of labels from users. User can specify their preferences on the performance objective regarding the three relevant metrics: approximation error, computation time, and labeling overheads. The ThalamusDB query optimizer chooses optimized plans according to user preferences, prioritizing data processing and requested labels to maximize impact. Experiments with several real-world data sets, taken from Craigslist, YouTube, and Netflix, show that ThalamusDB achieves an average speedup of 35.0x over MindsDB, an exact processing baseline, and outperforms ABAE, a sampling-based method, in 78.9% of cases.
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- Award ID(s):
- 2239326
- PAR ID:
- 10577787
- Publisher / Repository:
- ACM
- Date Published:
- Journal Name:
- Proceedings of the ACM on Management of Data
- Volume:
- 2
- Issue:
- 3
- ISSN:
- 2836-6573
- Page Range / eLocation ID:
- 1 to 26
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript
- Journal Article:
- https://doi.org/10.1145/3654989
