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Conference Paper
Exploiting Machine Learning Models for Approximate Query Processing
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- Authors
- Taewhi Lee, Kihyuk Nam, Choon Seo Park, Sung-Soo Kim
- Issue Date
- 2022-12
- Citation
- International Conference on Big Data (Big Data) 2022, pp.6734-6736
- Language
- English
- Type
- Conference Paper
- Abstract
- Approximate query processing can help reduce response time for aggregate queries in exploratory data analysis. In this study, we describe basic query transformation rules for processing approximate queries using synthetic data tables or inferential models. Based on the preliminary experimental results, we confirm that ML models can be used to provide approximate query results in response times acceptable for applications.
- KSP Keywords
- Approximate query processing, Exploratory Data Analysis, Query transformation, Synthetic data, Transformation rules, aggregate queries, machine learning models, response time