BROWSE
Detail
Conference Paper
Designing ML-based Approximate Query Processing Services on Time-Varying Large Dataset for Distributed Systems
Cited 1 time in 
Share 
Share
- Authors
- Kihyuk Nam, Sung-Soo Kim, Choon Seo Park, Taek Yong Nam, Taewhi Lee
- Issue Date
- 2022-10
- Citation
- International Conference on Information and Communication Technology Convergence (ICTC) 2022, pp.1979-1982
- Publisher
- IEEE
- Language
- English
- Type
- Conference Paper
- Abstract
- Approximate query processing (AQP) has been well established for big data analytics to complement performance degradation due to the ever-increasing size of datasets. The evolution of machine learning technologies creates another opportunity for improving the AQP. There are two architectural aspects that should be considered for AQP-based data analytics services to embrace the trends. First, the services should support rapidly changing data. Second, the systems should manage the life-cycle of the machine learning process and accommodate the diversity of ML technologies. This paper discusses the requirements and design considerations for ML-based AQP services on time-varying large datasets for distributed environments in a system-independent way.
- KSP Keywords
- Approximate query processing, Design considerations, Distributed Environment, Distributed System(DS), Large datasets, Learning Process, Life cycle, Machine learning technologies, big data analytics, performance degradation, time-varying