  Confidentiality hinders the publication of authentic, labeled datasets of
personal and enterprise data, although they could be useful for evaluating
knowledge graph construction approaches in industrial scenarios. Therefore, our
plan is to synthetically generate such data in a way that it appears as
authentic as possible. Based on our assumption that knowledge workers have
certain habits when they produce or manage data, generation patterns could be
discovered which can be utilized by data generators to imitate real datasets.
In this paper, we initially derived 11 distinct patterns found in real
spreadsheets from industry and demonstrate a suitable generator called Data
Sprout that is able to reproduce them. We describe how the generator produces
spreadsheets in general and what altering effects the implemented patterns
have.
