针对伺服阀生产过程中存在的设备种类繁多、不同供应商设备之间无法交换数据、数据集成工作复杂困难的问题,提出基于OPC UA (Object Linking and Embedding for Process Control Unified Architecture)和ETL (Extract-Transform-Load)...针对伺服阀生产过程中存在的设备种类繁多、不同供应商设备之间无法交换数据、数据集成工作复杂困难的问题,提出基于OPC UA (Object Linking and Embedding for Process Control Unified Architecture)和ETL (Extract-Transform-Load)的综合解决方案。该方案使用OPC UA作为通信协议完成设备之间的高效通信,利用ETL技术设计并实现了伺服阀综合应用系统。样机试验验证了方案的有效性。该方案实现了产线信息化过程中的设备互操作能力,是确保伺服阀质量可靠性和性能一致性的关键基础技术。展开更多
In this study, we delve into the realm of efficient Big Data Engineering and Extract, Transform, Load (ETL) processes within the healthcare sector, leveraging the robust foundation provided by the MIMIC-III Clinical D...In this study, we delve into the realm of efficient Big Data Engineering and Extract, Transform, Load (ETL) processes within the healthcare sector, leveraging the robust foundation provided by the MIMIC-III Clinical Database. Our investigation entails a comprehensive exploration of various methodologies aimed at enhancing the efficiency of ETL processes, with a primary emphasis on optimizing time and resource utilization. Through meticulous experimentation utilizing a representative dataset, we shed light on the advantages associated with the incorporation of PySpark and Docker containerized applications. Our research illuminates significant advancements in time efficiency, process streamlining, and resource optimization attained through the utilization of PySpark for distributed computing within Big Data Engineering workflows. Additionally, we underscore the strategic integration of Docker containers, delineating their pivotal role in augmenting scalability and reproducibility within the ETL pipeline. This paper encapsulates the pivotal insights gleaned from our experimental journey, accentuating the practical implications and benefits entailed in the adoption of PySpark and Docker. By streamlining Big Data Engineering and ETL processes in the context of clinical big data, our study contributes to the ongoing discourse on optimizing data processing efficiency in healthcare applications. The source code is available on request.展开更多
文摘针对伺服阀生产过程中存在的设备种类繁多、不同供应商设备之间无法交换数据、数据集成工作复杂困难的问题,提出基于OPC UA (Object Linking and Embedding for Process Control Unified Architecture)和ETL (Extract-Transform-Load)的综合解决方案。该方案使用OPC UA作为通信协议完成设备之间的高效通信,利用ETL技术设计并实现了伺服阀综合应用系统。样机试验验证了方案的有效性。该方案实现了产线信息化过程中的设备互操作能力,是确保伺服阀质量可靠性和性能一致性的关键基础技术。
文摘In this study, we delve into the realm of efficient Big Data Engineering and Extract, Transform, Load (ETL) processes within the healthcare sector, leveraging the robust foundation provided by the MIMIC-III Clinical Database. Our investigation entails a comprehensive exploration of various methodologies aimed at enhancing the efficiency of ETL processes, with a primary emphasis on optimizing time and resource utilization. Through meticulous experimentation utilizing a representative dataset, we shed light on the advantages associated with the incorporation of PySpark and Docker containerized applications. Our research illuminates significant advancements in time efficiency, process streamlining, and resource optimization attained through the utilization of PySpark for distributed computing within Big Data Engineering workflows. Additionally, we underscore the strategic integration of Docker containers, delineating their pivotal role in augmenting scalability and reproducibility within the ETL pipeline. This paper encapsulates the pivotal insights gleaned from our experimental journey, accentuating the practical implications and benefits entailed in the adoption of PySpark and Docker. By streamlining Big Data Engineering and ETL processes in the context of clinical big data, our study contributes to the ongoing discourse on optimizing data processing efficiency in healthcare applications. The source code is available on request.
