## 3 State of the Art  
The current state of the process models was researched under the focus of the above-mentioned requirements for an engineering-compatible approach in DM projects. In general, the methods searched for here are classified in the area of knowledge discovery in databases (KDD). KDD was coined as a term for a general process [7] in which knowledge is found from data with the help of data analysis algorithms. KDD comprises the following phases: (1) selection of target data to focus on, (2) preprocessing and cleaning data to obtain consistent data, (3) transformation to reduce dimensions of data, (4) data mining to search for patterns of interest, (5) interpretation and evaluation of mined patterns. Essentially, there are two common methodological approaches. These are, as described in [1, 8], the methods SEMMA and CRISP-DM, derived on the basis of KDD. SEMMA stands for sample, explore, modify, model, and assess. SEMMA was developed by the SAS Institute. SAS uses SEMMA to methodically organize the functions of its statistical and business intelligence software. CRISP-DM stands for CRoss-Industry Standard Process for Data Mining and is the result of a cooperation between IBM and other industrial companies. CRISP-DM is comprehensively documented [9] and serves as a general procedure for DM. In the SPSS software, CRISP-DM is used to manage DM projects. Both methods, SEMMA and CRISP-DM, are considered similar by [1, 8]. They differ at most in the emphasis of individual phases. Since both procedure models are implemented in software systems for DM that are widely used in each case, their application is also very widespread. At the detailed level, many different industry applications have emerged. An example is the adaptation of CRISP-DM in the medical field [10]. However, engineering adaptations of CRISP-DM also show the need for more systematic task-specific support when using DM or ML. For example, in [11], CRISP-DM is linked to Process Failure Mode and Effects Analysis (PFMEA) and Quality Function Deployment (QFD) methods to support the selection of data sources relevant to solving business objectives. The work in [12] takes up the challenges facing the manufacturing industry in selecting the appropriate algorithms for the use of machinery and process data and presents a guideline based on CRISP-DM. In summary, it can be stated that the well-known DM procedure models assume that the data is already available. Under this assumption, these approaches focus on data analysis. Domain-specific problems are not explicitly addressed. In preparation for the DM, the engineers must first take care of measuring the problem-relevant data in the correct quality in the production systems. This must be done in collaboration with the following KDD. Numerous projects have demonstrated the need to combine the skills of engineers and IT specialists and data analysts. In [13], this was already taken up and an extension of the workflow of CRISP-DM was suggested. The aim of the extension is to divide the extensive tasks of the engineers into independent phases. Thus, a better overview will facilitate the linkage to further aspects like to the project management. In [13], CRISP-DM was selected for the extension of KDD for engineering applications, because a stronger integration into the business strategy is supported. This feedback is considered essential for the success of DM in production, as DM projects in industry cause extensive organizational, manufacturing, and business impacts due to the interventions in the production systems, which need to be managed. On the other hand, the technical success of the DM must be directly transformed back into the economic dimension. This direct interrelation is usually lacking in the current planning fundamentals. This complicates the concrete understanding of benefits and inhibits project offensives. The concept of [13] is underpinned in Section 4 and illustrated in Section 5 with an example.