大数据2026，Vol.12Issue(5)：21-42,22.DOI:10.11959/j.issn.2096-0271.BDR26172
面向成本收益协同优化的高质量数据准备方法
A cost-benefit collaborative optimization method for high-quality data preparation
摘要
Abstract
In practical machine learning(ML)model training scenarios,data preparation is still constrained by low resource utilization efficiency,uncontrollable costs,and insufficient optimization under budget constraints.To address these problems,a holistic data preparation framework named CBAprep(cost-benefit-aware data preparation)was proposed,oriented toward cost-benefit collaborative optimization.Mathematical programming was adopted to incorporate data preprocessing,cleaning,transformation,and other operations into a unified cost modeling and decision-making framework,and a cost-benefit analysis and planning method for the overall pipeline was designed.Experimental results showed that CBAprep achieved a better trade-off between cost and performance under limited budget conditions,thereby improving the overall performance and cost-effectiveness of the data preparation process.The results indicate that incorporating cost-benefit analysis and optimization into data preparation strategies facilitates efficient resource allocation and provides useful support for cost control and performance optimization in complex data processing scenarios.关键词
数据治理/数据准备/资源调度/成本收益Key words
data governance/data preparation/resource scheduling/cost-benefit分类
信息技术与安全科学引用本文复制引用
崔文萱,丁小欧,程海峰,钱泽凯,李勇,王宏志,王晨..面向成本收益协同优化的高质量数据准备方法[J].大数据,2026,12(5):21-42,22.基金项目
国家科技重大专项(No.2025ZD1607102) （No.2025ZD1607102）
国家自然科学基金面上项目(No.62572151) （No.62572151）
黑龙江省自然科学基金优秀青年项目(No.YQ2024F005) （No.YQ2024F005）
黑龙江省青年科技人才托举工程项目(No.2025QNTJ001) （No.2025QNTJ001）
中央高校基本科研业务费专项资金资助(No.HITAI4SE2026035) National Science and Technology Major Project of China(No.2025ZD1607102),The National Natural Science Foundation of China General Program(No.62572151),Heilongjiang Provincial Natural Science Foundation Excellent Young Scholars Program(No.YQ2024F005),Heilongjiang Young Scientific and Technological Talent Support Program(No.2025QNTJ001),The Fundamental Research Funds for the Central Universities(No.HITAI4SE2026035) （No.HITAI4SE2026035）
