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학술대회 Designing ML-based Approximate Query Processing Services on Time-Varying Large Dataset for Distributed Systems
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저자
남기혁, 김성수, 박춘서, 남택용, 이태휘
발행일
202210
출처
International Conference on Information and Communication Technology Convergence (ICTC) 2022, pp.1979-1982
DOI
https://dx.doi.org/10.1109/ICTC55196.2022.9952398
협약과제
22HS4100, 빅데이터 대상의 빠른 질의 처리가 가능한 탐사 데이터 분석 지원 근사질의 DBMS 기술 개발, 이태휘
초록
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 제안 키워드
Approximate query processing, Big Data Analytics(BDA), Design considerations, Distributed Environment, Distributed System(DS), Large data sets, Life Cycle, Machine learning technologies, learning process, performance degradation, time-varying