  Typical legacy information systems store data in relational databases.
Process mining is a research discipline that analyzes this data to obtain
insights into processes. Many different process mining techniques can be
applied to data. In current techniques, an XES event log serves as a basis for
analysis. However, because of the static characteristic of an XES event log, we
need to create one XES file for each process mining question, which leads to
overhead and inflexibility. As an alternative, people attempt to perform
process mining directly on the data source using so-called intermediate
structures. In previous work, we investigated methods to build intermediate
structures on source data by executing a basic SQL query on the database.
However, the nested form in the SQL query can cause performance issues on the
database side. Therefore, in this paper, we propose a native SQL operator for
direct process discovery on relational databases. We define a native operator
for the simplest form of the intermediate structure, called the "directly
follows relation". This approach has been evaluated with big event data and the
experimental results show that it performs faster than the state-of-the-art of
database approaches.
