  Process mining discovers and analyzes a process model from historical event
logs. The prior art methods use the key attributes of case-id, activity, and
timestamp hidden in an event log as clues to discover a process model. However,
a user needs to specify them manually, and this can be an exhaustive task. In
this paper, we propose a two-stage key attribute identification method to avoid
such a manual investigation, and thus this is a step toward fully automated
process discovery. One of the challenging tasks is how to avoid exhaustive
computation due to combinatorial explosion. For this, we narrow down candidates
for each key attribute by using supervised machine learning in the first stage
and identify the best combination of the key attributes by discovering process
models and evaluating them in the second stage. Our computational complexity
can be reduced from $\mathcal{O}(N^3)$ to $\mathcal{O}(k^3)$ where $N$ and $k$
are the numbers of columns and candidates we keep in the first stage,
respectively, and usually $k$ is much smaller than $N$. We evaluated our method
with 14 open datasets and showed that our method could identify the key
attributes even with $k = 2$ for about 20 seconds for many datasets.
