首页> 外文会议>2012 IEEE 14th International Conference on e-Health Networking, Applications and Services. >Enhanced data extraction, transforming and loading processing for Traditional Chinese Medicine clinical data warehouse
【24h】

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

机译:中药临床数据仓库的增强数据提取,转换和加载处理

获取原文
获取原文并翻译 | 示例

摘要

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:临床数据仓库已被开发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结构。但是,作为关键组件,数据提取,转换和加载(ETL)是一项复杂且费力的工作,要在进行各种数据分析之前确保高质量的数据。本文介绍了一种增强的ETL技术框架,其中包括操作数据存储(ODS)模型和两步数据预处理子组件,以执行ETL任务。 ODS数据模型旨在集成异类临床数据源,并支持通过ETL将这些数据源直接复制到ODS数据库。因此,ETL任务已在增强的ETL组件中分为两个核心步骤:(1)动态过滤并将原始操作数据源复制到ODS; (2)将ODS数据专业转换为详细的临床数据仓库。这种增强的技术框架提高了用于临床数据中心的ETL性能,因为在此数据环境中将需要集成各种操作数据源。本文介绍了相关的增强ETL框架,并提出了一些完成任务的关键过程。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号