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Enhanced data extraction, transforming and loading processing for Traditional Chinese Medicine clinical data warehouse

机译:中医临床数据仓库增强的数据提取,转化和装载加工

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Clinical data warehouse has been developed as a fundamental data infrastructure for large scale TCM clinical data management and decision support services. However, as a key component, data extraction, transforming and loading (ETL) is a complicated and labor intensive task to ensure high data quality before all kinds of data analyses. This paper introduces an enhanced ETL technique framework, which includes operational data store (ODS) model and two step data preprocessing subcomponents, to perform the ETL tasks. The ODS data model was designed to integrate the heterogeneous clinical data sources and support the direct copy from these data sources to ODS database by ETL. Therefore, ETL task has been separated into two core steps in enhanced ETL component: (1) dynamic filter and copy of the original operational data sources to ODS; (2) specialized transforming the ODS data to detailed clinical data warehouse. This enhanced technique framework improves the ETL performance to be used in clinical data center since there would have various kinds of operational data sources that need be integrated in this data environments. This paper has a description of the related enhanced ETL framework and proposes some key procedures to accomplish the tasks.
机译:临床数据仓库已成为大规模中医临床数据管理和决策支持服务的基本数据基础设施。然而,作为关键组件,数据提取,转换和加载(ETL)是一种复杂的和劳动密集型任务,以确保在各种数据分析之前的高数据质量。本文介绍了增强的ETL技术框架,包括操作数据存储(ODS)模型和两个步骤数据预处理子组件,以执行ETL任务。 ODS数据模型旨在集成异构临床数据源,并通过ETL从这些数据源的直接复制到ODS数据库。因此,ETL任务已分为增强ETL组件中的两个核心步骤:(1)动态滤波器和原始操作系统源的副本到ODS; (2)专门将ODS数据转换为详细的临床数据仓库。这种增强的技术框架改善了在临床数据中心中使用的ETL性能,因为存在各种需要在此数据环境中集成的操作数据源。本文描述了相关的增强型ETL框架,并提出了一些关键程序来完成任务。

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