  Process mining is a field of computer science that deals with discovery and
analysis of process models based on automatically generated event logs.
Currently, many companies use this technology for optimization and improving
their processes. However, a discovered process model may be too detailed,
sophisticated and difficult for experts to understand. In this paper, we
consider the problem of discovering a hierarchical business process model from
a low-level event log, i.e., the problem of automatic synthesis of more
readable and understandable process models based on information stored in event
logs of information systems.
  Discovery of better structured and more readable process models is
intensively studied in the frame of process mining research from different
perspectives. In this paper, we present an algorithm for discovering
hierarchical process models represented as two-level workflow nets. The
algorithm is based on predefined event ilustering so that the cluster defines a
sub-process corresponding to a high-level transition at the top level of the
net. Unlike existing solutions, our algorithm does not impose restrictions on
the process control flow and allows for concurrency and iteration.
