## 4 Evaluation  
The evaluation is based on two workshops and seven expert interviews described below. The profiles of experts consulted for evaluation are outlined in Table 4. The experts for interviews and workshops were chosen in a way that a broad range of professional backgrounds of actors relevant for SSSE as well as different company sizes are represented. The resulting group of chosen experts, therefore, ranges from academic to small and big companies. The sectors are focused on mobility, but the experts from academia have a history of researching a broad range of engineering-focused sectors. Overall, the majority of experts come from industry (7 out of 12). The following experts share the same employer: Experts 1, 6, and 10; experts 2 and 8; experts 3, 4, and 5; experts 7 and 9. The specialization of the experts covers all professional backgrounds relevant to the SemDaServ approach, ranging from the business perspective (expert IDs 5, 7, 11), to the SSSE perspective (expert IDs 1, 2, 8), the MBSE perspective (expert ID 6) a highly specialized knowledge carrier perspective (expert ID 12), the data science and data engineering perspective (expert IDs 3, 4, 7, 9), and the knowledge management in engineering perspective (expert ID 10). The professional experience of the experts also spans a wide range from more than two years up to more than 40 years of professional experience. \begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**ID** & **Job Title** & **Specialization** & **Professional** & **Company Type** & **Sector** \\  & & & **Experience** & & & \\ \hline
1 & Research assistant & Smart service platforms, & \textgreater{}2 years & Technical university & Academia \\  & & Internet-of-Things, & & (\textgreater{}7.500 employees) & \\  & & cloud computing & & & \\
2 & Research engineer & Smart service systems & \textgreater{}3 years & Research institute for & Academia \\  & & engineering, Internet-of-Things & & applied science & \\  & & & & (\textgreater{}25.000 employees) & \\
3 & Data scientist & Smart services, natural language & \textgreater{}3 years & Big company & Rail \\  & & processing, condition monitoring & & (\textgreater{}30.000 employees) & \\
4 & Data scientist & Artificial intelligence, operations & \textgreater{}6 years & Big company & Rail \\  & & research, data engineering & & (\textgreater{}30.000 employees) & \\
5 & Principal key & Project management, & \textgreater{}20 years & Big company & Rail \\  & expert & business strategy & & (\textgreater{}30.000 employees) & \\
6 & Research assistant & Model-based systems engineering & \textgreater{}3 years & Technical university & Academia \\  & & & & (\textgreater{}7.500 employees) & \\
7 & IT project & Artificial intelligence, production & \textgreater{}4 years & Big company & Automotive \\  & manager & planning, six sigma & & (\textgreater{}150.000 employees) & \\
8 & Researcher and & Smart service systems & \textgreater{}7 years & Research institute for & Academia \\  & managing director & engineering, product & & applied science & \\  & & lifecycle management & & (\textgreater{}25.000 employees) & \\
9 & Doctoral & Artificial intelligence and & \textgreater{}6 years & Big company & Automotive \\  & candidate & digital twins & & (\textgreater{}150.000 employees) & \\
10 & Research assistant & Knowledge management & \textgreater{}8 years & Technical university & Academia \\  & & in engineering & & (\textgreater{}7.500 employees) & \\
11 & CEO & Innovation, management and & \textgreater{}30 years & Small company & Consulting \\  & & technology consulting & & (\textless{}20 employees) & \\
12 & System architect & Predictive maintenance, engine & \textgreater{}40 years & Big company & Aerospace \\  & & health monitoring, & & (\textgreater{}50.000 employees) & \\  & & system architecture & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Profiles of experts consulted for evaluation of the SemDaServ approach. The first workshop (participating experts: IDs 1 and 2; cf.