Process Discovery using Inductive Miner and Decomposition
  This report presents a submission to the Process Discovery Contest. The
contest is dedicated to the assessment of tools and techniques that discover
business process models from event logs. The objective is to compare the
efficiency of techniques to discover process models that provide a proper
balance between "overfitting" and "underfitting". In the context of the Process
Discovery Contest, process discovery is turned into a classification task with
a training set and a test set; where a process model needs to decide whether
traces are fitting or not. In this report, we first show how we use two
discovery techniques, namely: Inductive Miner and Decomposition, to discover
process models from the training set using ProM tool. Second, we show how we
use replay results to 1) check the rediscoverability of models, and to 2)
classify unseen traces (in test logs) as fitting or not. Then, we discuss the
classification results of validation logs, the complexity of discovered models,
and their impact on the selection of models for submission. The report ends
with the pictures of the submitted process models.
