Query Transformation for Approximate Query Processing Using Synthetic Data from Deep Generative Models
- Resource Type
- Conference
- Authors
- Lee, Taewhi; Park, Choon Seo; Nam, Kihyuk; Kim, Sung-Soo
- Source
- 2022 IEEE International Conference on Consumer Electronics-Asia (ICCE-Asia) Consumer Electronics-Asia (ICCE-Asia), 2022 IEEE International Conference on. :1-4 Oct, 2022
- Subject
- Aerospace
Communication, Networking and Broadcast Technologies
Computing and Processing
Engineering Profession
Fields, Waves and Electromagnetics
Geoscience
Photonics and Electrooptics
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Transportation
Productivity
Data analysis
Query processing
Aggregates
Data models
Space exploration
Low latency communication
Approximate Query Processing
Generative Model
Synthetic Data
- Language
In exploratory data analysis, interactive latency can make a significant impact on the data exploration space and user productivity. To provide low latency for the aggregation query, approximate query processing can be considered as a possible alternative. In this paper, we describe the transformation rules for processing approximation queries. We present the preliminary experimental results on the performance of approximate query processing with synthetic data.