大数据2026，Vol.12Issue(4)：61-80,20.DOI:10.11959/j.issn.2096-0271.2026021
面向工业时序数据管理与分析融合的数据模型转换
A data model transformation approach for integrated management and analysis of industrial time-series data
摘要
Abstract
In industrial internet of things(IIoT)scenarios,device monitoring data are typically stored in the form of time-series data and modeled using hierarchical path structures to facilitate point management and access;however,this modeling approach does not directly support multidimensional analysis based on relational models and often requires complex ETL transformations.To address the model-level conflict between time-series data management and analysis in industrial settings,this paper proposes a unified time-series data model.The model leverages a hierarchical tree structure for flexible data ingestion and storage,while offering multidimensional analytical capabilities through a classical relational model view.We implement this approach systematically in the industrial time-series database Apache IoTDB,enabling a single dataset to support both monitoring and analytical requirements.关键词
工业物联网/时序数据库/数据模型/元数据管理/层次数据/模型转换Key words
industrial internet of things/timeseries database/data model/schema management/hierarchical data/data model transformation分类
信息技术与安全科学引用本文复制引用
李烁麟,林欣涛,田原,许京奕,陈荣钊,李永瑾,乔嘉林..面向工业时序数据管理与分析融合的数据模型转换[J].大数据,2026,12(4):61-80,20.基金项目
国家自然科学基金项目(No.62021002) The National Natural Science Foundation of China(No.62021002) （No.62021002）
