  Enterprise information systems allow companies to maintain detailed records
of their business process executions. These records can be extracted in the
form of event logs, which capture the execution of activities across multiple
instances of a business process. Event logs may be used to analyze business
processes at a fine level of detail using process mining techniques. Among
other things, process mining techniques allow us to discover a process model
from an event log -- an operation known as automated process discovery. Despite
a rich body of research in the field, existing automated process discovery
techniques do not fully capture the concurrency inherent in a business process.
Specifically, the bulk of these techniques treat two activities A and B as
concurrent if sometimes A completes before B and other times B completes before
A. Typically though, activities in a business process are executed in a true
concurrency setting, meaning that two or more activity executions overlap
temporally. This paper addresses this gap by presenting a refined version of an
automated process discovery technique, namely Split Miner, that discovers true
concurrency relations from event logs containing start and end timestamps for
each activity. The proposed technique is also able to differentiate between
exclusive and inclusive choices. We evaluate the proposed technique relative to
existing baselines using 11 real-life logs drawn from different industries.
