  The Alpha algorithm was the first process discovery algorithm that was able
to discover process models with concurrency based on incomplete event data
while still providing formal guarantees. However, as was stated in the original
paper, practical applicability is limited when dealing with exceptional
behavior and processes that cannot be described as a structured workflow net
without short loops. This paper presents the Alpha+++ algorithm that overcomes
many of these limitations, making the algorithm competitive with more recent
process mining approaches. The different steps provide insights into the
practical challenges of learning process models with concurrency, choices,
sequences, loops, and skipping from event data. The approach was implemented in
ProM and tested on various publicly available, real-life event logs.
