  In the age of Industry 4.0 and Cyber-Physical Production Systems (CPPSs) vast
amounts of potentially valuable data are being generated. Methods from Machine
Learning (ML) and Data Mining (DM) have proven to be promising in extracting
complex and hidden patterns from the data collected. The knowledge obtained can
in turn be used to improve tasks like diagnostics or maintenance planning.
However, such data-driven projects, usually performed with the Cross-Industry
Standard Process for Data Mining (CRISP-DM), often fail due to the
disproportionate amount of time needed for understanding and preparing the
data. The application of domain-specific ontologies has demonstrated its
advantageousness in a wide variety of Industry 4.0 application scenarios
regarding the aforementioned challenges. However, workflows and artifacts from
ontology design for CPPSs have not yet been systematically integrated into the
CRISP-DM. Accordingly, this contribution intends to present an integrated
approach so that data scientists are able to more quickly and reliably gain
insights into the CPPS. The result is exemplarily applied to an anomaly
detection use case.
