Data is Moody: Discovering Data Modification Rules from Process Event
  Logs
Marco Bjarne Schuster Boris Wiegand Jilles Vreeken
Although event logs are a powerful source to gain insight about the behavior
of the underlying business process, existing work primarily focuses on finding
patterns in the activity sequences of an event log, while ignoring event
attribute data. Event attribute data has mostly been used to predict event
occurrences and process outcome, but the state of the art neglects to mine
succinct and interpretable rules how event attribute data changes during
process execution. Subgroup discovery and rule-based classification approaches
lack the ability to capture the sequential dependencies present in event logs,
and thus lead to unsatisfactory results with limited insight into the process
behavior.
  Given an event log, we are interested in finding accurate yet succinct and
interpretable if-then rules how the process modifies data. We formalize the
problem in terms of the Minimum Description Length (MDL) principle, by which we
choose the model with the best lossless description of the data. Additionally,
we propose the greedy Moody algorithm to efficiently search for rules. By
extensive experiments on both synthetic and real-world data, we show Moody
indeed finds compact and interpretable rules, needs little data for accurate
discovery, and is robust to noise.