Simulation-Based Data Acquisition

In data science, the application of most approaches requires the existence of big data from a real-world system. Due to access limitations, nonexistence of the system, or temporal as well as economic restrictions, such data might not be accessible or available. To overcome a lack of real-world data, this chapter introduces simulation-based data acquisition as method for the generation of artificial data that serves as a substitute when applying data science techniques. Instead of gathering data from the real-world system, computer simulation is used to model and execute artificial systems that can provide a more accessible, economic, and robust source of big data. To this end, it is outlined how data science can benefit from simulation and vice versa. Specific approaches are introduced for the design and execution of experiments, and a selection of simulation frameworks is presented that facilitates the conducting of simulation studies for novice and professional users