Industry 4.0 factories are complex and data-driven. Data is yielded from many sources, including sensors, PLCs, and other devices, but also from IT, like ERP or CRM systems. We ask how to collect and process this data in a way, such that it includes metadata and can be used for industrial analytics or to derive intelligent support systems. This paper describes a new, query model based approach, which uses a big data architecture to capture data from various sources using OPC UA as a foundation. It buffers and preprocesses the information for the purpose of harmonizing and providing a holistic state space of a factory, as well as mappings to the current state of a production site. That information can be made available to multiple processing sinks, decoupled from the data sources, which enables them to work with the information without interfering with devices of the production, disturbing the network devices they are working in, or influencing the production process negatively. Metadata and connected semantic information is kept throughout the process, allowing to feed algorithms with meaningful data, so that it can be accessed in its entirety to perform time series analysis, machine learning or similar evaluations as well as replaying the data from the buffer for repeatable simulations.

翻译：工业4.0工厂是复杂且数据驱动的。数据来源于众多渠道，包括传感器、PLC及其他设备，也来源于IT系统，如ERP或CRM系统。我们探讨如何以包含元数据的方式收集和处理这些数据，使其可用于工业分析或衍生智能支持系统。本文描述了一种基于查询模型的新方法，该方法以OPC UA为基础，利用大数据架构从多种数据源捕获数据。它通过缓冲和预处理信息，旨在协调并提供工厂的整体状态空间，以及映射到生产现场的当前状态。这些信息可供多个处理终端使用，且与数据源解耦，从而使这些终端能够处理信息而不干扰生产设备、不影响其所处的网络设备，或对生产过程产生负面影响。在整个过程中保留元数据和相关语义信息，从而能够为算法提供有意义的数据，以便完整访问这些数据以执行时间序列分析、机器学习或类似评估，以及从缓冲区重放数据以进行可重复的仿真。