
TL;DR
This paper reviews and compares various process mining algorithms, highlighting their input parameters, techniques, and outputs, to enhance understanding of process analysis using event logs in business systems.
Contribution
It provides a comprehensive comparison of process mining algorithms, emphasizing their methodologies and outputs, aiding researchers and practitioners in selecting appropriate techniques.
Findings
Different process mining algorithms vary in input requirements and techniques.
Algorithms produce diverse process models suited for various analysis tasks.
Comparison helps identify suitable algorithms for specific process analysis needs.
Abstract
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…
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Taxonomy
TopicsBusiness Process Modeling and Analysis · Manufacturing Process and Optimization · Drilling and Well Engineering
