  Process-Aware Information Systems (PAIS) is an IT system that support
business processes and generate large amounts of event logs from the execution
of business processes. An event log is represented as a tuple of CaseID,
Timestamp, Activity and Actor. Process Mining is a new and emerging field that
aims at analyzing the event logs to discover, enhance and improve business
processes and check conformance between run time and design time business
processes. The large volume of event logs generated are stored in the
databases. Relational databases perform well for a certain class of
applications. However, there are a certain class of applications for which
relational databases are not able to scale. To handle such class of
applications, NoSQL database systems emerged. Discovering a process model
(workflow model) from event logs is one of the most challenging and important
Process Mining task. The $\alpha$-miner algorithm is one of the first and most
widely used Process Discovery technique. Our objective is to investigate which
of the databases (Relational or NoSQL) performs better for a Process Discovery
application under Process Mining. We implement the $\alpha$-miner algorithm on
relational (row-oriented) and NoSQL (column-oriented) databases in database
query languages so that our algorithm is tightly coupled to the database. We
present a performance benchmarking and comparison of the $\alpha$-miner
algorithm on row-oriented database and NoSQL column-oriented database so that
we can compare which database can efficiently store massive event logs and
analyze it in seconds to discover a process model.
