## 4 Technologies to Support Contextualization
### Sampling and Integrating Process Data
#### 4.2.2 Integrating Data from Different Sources  
To support contextualization, three aspects of integrating data from different sources should be considered: horizontal integration, vertical integration, and the integration of static and dynamic data. _Horizontal integration_ means that data from different devices in different parts of the plant are brought together. For instance, the speed of a chocolate packaging machine can be placed in relation to the temperature in a cooler of the previous molding unit. This is a technical challenge, because machines from different vendors cannot easily communicate with each other. Traditionally, this communication has been realized via fieldbus technologies [143]. These standards describe how data are to be handled. An advantage of many fieldbusses is that they can process data in real time. However, a problem is that they have no standardized semantic capabilities. This issue can be tackled with OPC UA, which not only provides an information model (see above) but also a communication standard defining how data are exchanged between different machines. In addition to the cable-based standards, there are first approaches to using the 5 th generation mobile radio standard (5G) for industrial applications [144]. This wireless technology makes it possible to distribute data throughout the plant without any need for physical connections between different sensors and actuators. In line with retrofitting and NOA, this enables a use of sensor data beyond what was planned when originally designing the plant. Moreover, 5G enables real-time communication with low latencies and high throughput.  
The second aspect of integrating data is _vertical integration_: connecting different levels in the automation pyramid with its field, control, supervisory, planning, and management levels. That is, when interpreting low-level data from a specific sensor (e.g., temperature), it might be necessary to contextualize them with high-level information about production planning (e.g., type of chocolate, current recipe). Vertical integration is supported by the NAMUR Open Architecture (NOA) [145], which describes how data from each level of the automation pyramid can be made available to other levels via open interfaces such as OPC UA. Moreover, integration with data from outside the plant (e.g., forecasts of energy prices) and integration across different agents (e.g., co-workers, customers) might be needed.  
A _combination of static and dynamic data_ is beneficial for horizontal integration and is the very core of vertical integration. It implies that completely different technologies have to interact with each other. For instance, combining OPC UA with Linked Enterprise Data [146] makes it possible to use the models or ontologies described above to aid in the selection and interpretation of dynamic sensor data. Consider the following fault scenario at a chocolate packaging machine. The fault is known to be connected to melting chocolate (via fault ontologies); it is known that temperatures from the molding unit affect the packaging process (via ontologies of relationships between production steps), and it is known that there is a temperature sensor in the cooler of the molding unit (via plant ontologies). Given this knowledge, it is possible to receive the current temperature from the relevant sensor (via OPC UA or 5G) and relate it to the speed of the packaging machine. Thus, current process data can be selected and interpreted in the light of its relations to data from other production steps. In this way, context can be provided across technology boundaries. Moreover, operators can be informed when data that are important according to the ontologies (e.g., state of packaging material) are unavailable.