## 1 Introduction  
For smart services to be developed and successfully operated as a component of smart service system (SSS), a qualitatively and quantitatively sufficient amount of data is required. This is especially true when data-driven software components are implemented by using statistical methods from the field of artificial intelligence: the quality of the training data directly determines the quality of the trained AI model. However, the quality of the training data can only be evaluated at a late stage using existing methods in the context of smart service development, since the training success is only known when the artificial intelligence (AI) training takes place. Furthermore, automated machine learning (AutoML) approaches are rising [1, 2, 3, 4, 5, 6, 7, 8, 9]. AutoML is aiming for the automation of machine learning (ML) model development. This means that SSS development projects using AutoML will need to rely even more on sufficient data carrying the relevant information because the ML model created with AutoML relies completely on statistics on the raw data and ignores causality only available from domain experts. Therefore, the availability of sufficient data and especially data engineering [10, 11] will stay a major bottle-neck of AI applications. If the information in the training data or the data to be analyzed in operations is missing, a statistical AI model will be of poor quality. If irrelevant data is fed into AutoML pipelines, spurious correlations could result in unpredictable and dangerous product behavior occurring during the operation of the SSS. The purpose of the presented Semantic Data Specification for AI-Based Smart Services (SemDaServ) approach is to specify the data needs of an AI-based smart service as part of a SSS as early as possible using expert domain knowledge. This allows the identification of insufficient data quality and quantity without the need to train AI models. This is often necessary for practice if the data for a smart service in development is not yet available and therefore needs to be acquired. To acquire relevant data while omitting irrelevant data (and therefore saving time and cost) data specification describing the data needs of smart service's AI and other software components is required. Furthermore, it is crucial to identify all areas of smart service's data needs; an unidentified data need may require changing the physical product (e.g., integrating new sensors into the main bearings of an aircraft turbine)--which could result in high costs and delays. To make the domain knowledge of experts available to smart service and AI engineers, the domain knowledge is formalized in a way that preserves the semantic meaning of the data specified. Take, for example, the service-oriented Power-by-the-Hour business model for aerospace engines, where the customers pay for hours using the engine while not owning the engine. The engine provider (SSS provider) needs to make sure that the customers always got an operational engine at the wing. If a critical component like a bearing is going to fail soon, this must be immediately known to the engine provider to trigger actions for maintenance or exchange of the engine. Therefore, the remaining life prediction for the bearing is a critical component of the SSS offering. Within this article, this kind of remaining life prediction is understood as a smart service being part of a SSS. The structure of the article is presented in Figure 1: (A) Based on the state of the research field and resulting research gaps described in Section 1, research questions, working hypotheses, and research methods are described in Section 2. (B) Additionally, the purpose of the SemDaServ approach described in Section 1 and (C) the research questions described in Section 2 are setting the scope for Section 3.