  System logs perform a critical function in software-intensive systems as logs
record the state of the system and significant events in the system at
important points in time. Unfortunately, log entries are typically created in
an ad-hoc, unstructured and uncoordinated fashion, limiting their usefulness
for analytics and machine learning. In this paper, we present the main
challenges of contemporary approaches to generating and storing system logs
data for large, complex, software-intensive systems based on an in-depth case
study at a world-leading telecommunications company. Second, we present a
systematic and structured approach for generating log data that does not suffer
from the aforementioned challenges and is optimized for use in machine
learning. Third, we provide validation of the approach based on expert
interviews that confirms that the approach addresses the identified challenges
and problems.
