Exploiting Machine Learning Models for Approximate Query Processing
- Resource Type
- Conference
- Authors
- Lee, Taewhi; Nam, Kihyuk; Park, Choon Seo; Kim, Sung-Soo
- Source
- 2022 IEEE International Conference on Big Data (Big Data) Big Data (Big Data), 2022 IEEE International Conference on. :6752-6754 Dec, 2022
- Subject
- Communication, Networking and Broadcast Technologies
Computing and Processing
Engineering Profession
Geoscience
Robotics and Control Systems
Signal Processing and Analysis
Data analysis
Query processing
Aggregates
Machine learning
Big Data
Data models
Time factors
approximate query processing
generative model
inferential model
synthetic data
- Language
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.