  Checking various log files from different processes can be a tedious task as
these logs contain lots of events, each with a (possibly large) number of
attributes. We developed a way to automatically model log files and detect
outlier traces in the data. For that we extend Dynamic Bayesian Networks to
model the normal behavior found in log files. We introduce a new algorithm that
is able to learn a model of a log file starting from the data itself. The model
is capable of scoring traces even when new values or new combinations of values
appear in the log file.
