  Process mining is a new emerging research trend over the last decade which
focuses on analyzing the processes using event log and data. The raising
integration of information systems for the operation of business processes
provides the basis for innovative data analysis approaches. Process mining has
the strong relationship between with data mining so that it enables the bond
between business intelligence approach and business process management. It
focuses on end to end processes and is possible because of the growing
availability of event data and new process discovery and conformance checking
techniques. Process mining aims to discover, monitor and improve real processes
by extracting knowledge from event logs readily available in todays information
systems. The discovered process models can be used for a variety of analysis
purposes. Many companies have adopted Process aware Information Systems for
supporting their business processes in some form. These systems typically have
their log events related to the actual business process executions. Proper
analysis of Process Aware Information Systems execution logs can yield
important knowledge and help organizations improve the quality of their
services. This paper reviews and compares various process mining algorithms
based on their input parameters, the techniques used and the output generated
by them.
