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.

基于事件日志，我们旨在找到准确而简洁可解释的 if-then 规则，描述过程如何修改数据。我们使用 MDL 原则正式定义问题，并提出贪婪的 Moody 算法来高效搜索规则。通过对合成的和真实世界数据的广泛实验，我们展示了 Moody 确实可以发现简洁和可解释的规则，只需要少量数据进行准确发现，并且对噪声具有鲁棒性。