## 3 The SemDaServ Approach  
The activity of semantic data specification of smart services is at the center of the proposed SemDaServ approach presented in Figure 4. The guidance to successfully conduct this activity is primarily provided by the SemDaServ process dimension: The three-step data specification process (Clarify domain knowledge needs, Clarify information needs, Specify data needs) systematically guides the actors involved through the individual steps of data specification and ensures that the knowledge pyramid is systematically traversed starting from domain knowledge (definition: information plus context, experience and cross-linking [34]), to information (definition: data plus meaning [34]) needs, and then to data (definition: symbols plus syntax [34]) needs (cf. [21]). As the SemDaServ approach is generally based on traversing the knowledge pyramid, the SemDaServ is applicable from new development of a smart service to continuous integration (CI) and continuous delivery (CD) scenarios. Realizing SemDaServ is expected to result in (1) a clear understanding of relevant data for the development and operation of the smart service, (2) formalized domain knowledge relevant for AI training, and (3) increased probability of AI development with quality of results in line with smart service requirements. The Clarify information needs process step is the most important step here, since the information level is where knowledge and data come together. Without the information level, it is difficult to infer concrete data needs (e.g., vibration sensor signal sampled at 200 Hz at the uppermost point of the outer ring) from general domain knowledge (e.g., damage in the bearing causes vibrations in the system). Here, the information layer serves to formalize the relevant domain knowledge in the context of the system (e.g., increasing vibration at the outer ring implies increasing wear of the ball bearings). Figure 4: Overview of the SemDaServ approach. Various artifacts are used and created as part of the data specification process. On the one hand, these artifacts serve to facilitate individual process steps by specifying the result formats in a structured manner. On the other hand, the artifacts also document the process results. The artifacts can be divided into two groups: (1) artifacts for describing data and information and (2) artifacts for supporting neural points during process execution. Group 1 artifacts use SysML for three following reasons. First, computer scientists and data scientists benefit from semantic data specification due to the formalized domain knowledge, and SysML's proximity to Unified Modeling Language (UML), which is commonly used in SSSE [15], is useful here. Next, SysML is a powerful modeling language that can represent arbitrary technical systems and software components. This makes it possible to describe the connection between the smart service and the physical holistically. Third, the increasing diffusion of model-based systems engineering (MBSE) makes it likely that the diffusion of SysML will further grow as well, and will be increasingly supported by IT tools. Thus, in an MBSE environment, semantic data specification for smart services can be carried out without media discontinuity in the context of system specifications, and can be seamlessly integrated into the model-based engineering of the future. Tools and IT systems increase the efficiency and quality of artifact creation. For example, visualization and modeling tools support the creation of SysML models. Text and spreadsheet tools enable the digital creation of documentation, such as tables and checklists. Theoretically, the creation of SysML models, checklists, and tables is also possible in a paper-based manner. However, using software tools for these activities is much more convenient, avoids errors, and increases process efficiency.