## 2 Requirements for the New Methodology  
The experiences from previous projects have shown that in the area of engineering several important tasks have to be processed in the DM project, which go far beyond pure data analysis. As described in the introduction, data is often not available by default and the DM methodologies do not support guidance in data collection. Therefore, the data needs to be collected first by choosing suitable sensors and data acquisition methods. The necessary signals and required data quality are directly connected to the research question of the project. Data acquisition as well as the traceability and use of DM results are integral components of production systems and processes. Here, the DM results unfold their economic dimension and here they are directly safety-relevant. This means that the installation of DM-relevant software and hardware must not disrupt any processes or cause any damage to people, the environment, or resources. For this reason, DM projects have to be handled holistically by engineers. From an engineer's point of view, this leads to the following requirements for a holistic approach in DM:  
* First, a transformation of the business objectives into a technical task is required to clearly define technical strategies to achieve the business goals [1].
* Methods are needed to derive relevant influencing parameters (e.g., load, condition) on the target variables. First, it has to be worked out which parameters have to be measured. The more complex the system interactions become, the more important a systematic approach becomes [5]. Methods such as Ishikawa diagrams, Pareto diagrams, or cause-effect diagrams can help here.
* The development of a suitable measurement and data acquisition technology requires a link to the targeted DM algorithms. For the selection of measurement methods and associated sensors, technical performance parameters must be known which depend not insignificantly on the data quality required by the DM algorithm. This also applies to the data transmission path [6].
* The DM project must take into account the conditions of a running production which must not be disturbed by the DM project. The approach of the engineers often considers this with proof of concepts or testbeds. These are used to develop a first working solution outside production. After that, the implementation into the production systems takes place.
* The engineering knowledge on the behavior of the machines and the interactions in the processes is to be incorporated into the DM strategy. Usually, new connections are not the main subject of the search, but previous knowledge is reproduced and perfected with the DM investigations. In order to keep the effort of complex investigations targeted and low, methods of designs of experiments can be used. Thus, according to the analysis task, targeted parameter specifications are given in the test series and more complex correlations can be uncovered with fewer tests.
* In general, it is necessary that an engineer-suitable procedure for DM brings together the necessary expertise for the analysis and to the IT with the domain-specific knowledge of the manufacturing. Since the occurring DM applications are very diverse, a generic procedure model is needed for data mining in production engineering.
* The holistic DM approach must cover the whole value chain from planning and implementation of the DM solution to documentation, permanent success control, and continuous improvement.
* A simple applicability of this holistic approach for the engineers is also desirable. Supporting libraries for the situational optimal selection of measurement methods and sensors or of the problem-oriented evaluation algorithms can contribute to this. Also helpful would be integrated knowledge memories for lessons learnt or best practices. These modules can be based on the extended methodical procedure for DM and can be used in further development work for an assistance software.