## 3 Results
### Use Case 2: Identification of Suitable Methods  
The central aspect, and a further extension of use case 1, is the identification of suitable methods for problems found in individual product development processes. Therefore, the ontology is used to query the stored _Data_ and _UseCase_. During a process optimisation process, the project leader analyses the given process to identify weak spots. Based on the relevant _SubProcess_, it is possible to identify the available _Data_--provided that they were collected. Combined with the _TaskCluster_, suitable _DataDrivenTasks_, _DataDrivenMethods_ and _Tools_ can be identified.  
The following initial situation is assumed. A simulation department is identified as a bottleneck in the _ProductDevelopmentProcess_; in particular, iterations with the design department have led to a high workload and time delays. This is especially critical relatively early in the product development process since many different concepts are being evaluated quickly. The relevant _SubProcess_ is to transfer the configuration of a design, represented in a parametric CAD file, into a simulation, which calculates and generates the results. The associated _Data_ are stored in a simulation data management (SDM) system as simulation results. The results can be broken down into numerical displacement information. For training, a high-performance graphic processing unit (GPU)-based machine is available. The later usage should be possible with low computing power and through a command line solution. Table 2 gives a summary of the formal description.  
Transferring this description into a query based on the SPARQL 1.1 query language allows filtering the knowledge base for suitable methods and tools to support this process. The used query is shown in Listing 1.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Concept** & **Value** \\ \hline Team & Simulation \\ SubProcess & Simulation \\ DataIn.Format & CAD-Data \(\cap\) Numerical parameters \\ DataIn.Storage & PDM System \\ DataOut.Format & FEM Result \(\cap\) Numerical value \\ DataOut.Storage & SDM System \\ TrainingHardware & GPU-based \\ UsageHardware & low computing \(\cup\) command line \\ \hline \hline \end{tabular}
\end{table}
Table 2: Formal problem description.  
```
SELECT?DataDrivenTask,?DataDrivenMethod,?DataDrivenToolWHHERE{?AI4 PD:TeamAI4 PD:hasValueSimulationDepartment?AI4 PD:SubProcessAI4 PD:hasValueSimulation?AI4 PD:DataIn;AI4 PD:hasFormatAI4 PD:CADData||AI4 PD:CSVDataAI4 PD:hasStorageAI4 PD:PDM-System?AI4 PD:DataOut;AI4 PD:hasFormatAI4 PD:FEMResult||AI4 PD:CSVDataAI4 PD:hasStorageAI4 PD:SDM-System?AI4 PD:TrainingHardwareAI4 PD:hasValueGPUBased?AI4 PD:UsageHardware;AI4 PD:hasComputingPowerAI4 PD:lowComputing&&AI4 PD:hasInterfaceAI4 PD:commandLine }
```  
The SELECT expression queries three variables, which are filtered using the WHERE expression. The filter searches for the conditions in Table 2.  
The output shows the used methods and tools from SLASSY since this use case aims at supporting the described problem. Additionally, this is the answer for the competency question from use case 2 in Section 2.3.  
The output table in Table 3 shows the identified suitable method. It is possible to identify more than one possibility, since a _DataDrivenMethod_ can be implemented in different _Tools_. In this case, further filtering should be performed based on licensing, for example, to only consider freeware. This should be carried out depending on the goals or internal requirements of the company.