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Process mining is a family of techniques to discover business process models and other knowledge of business processes from event logs. Existing process mining techniques are geared towards discovering models that capture the order of execution of tasks, but not the conditions under which tasks are executed --- also called branching conditions. One existing process mining technique, namely ProM's Decision Miner, applies decision tree learning techniques to discover branching conditions composed of atoms of the form \"v op c\" where \"v\" is a variable, \"op\" is a comparison predicate and \"c\" is a constant. This paper puts forward a more general technique to discover branching conditions where the atoms are linear equations or inequalities involving multiple variables and arithmetic operators. The proposed technique combine invariant discovery techniques embodied in the Daikon system with decision tree learning techniques.

Discovering Branching Conditions from Business Process Execution Logs