What Is Process Mining?

Process mining (the bridge between data mining and business process management) is a relatively young discipline. It lies between machine learning and data mining on the one hand, and process modeling and analysis on the other. The concept of process mining is to discover, monitor, and improve actual business processes by extracting knowledge from event logs.

Process mining

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Process mining (the bridge between data mining and business process management) is a relatively young discipline. It lies between machine learning and data mining on the one hand, and process modeling and analysis on the other. The concept of process mining is to discover, monitor, and improve actual business processes by extracting knowledge from event logs.
Chinese name
Process mining
Foreign name
Process Mining
Full name
Business process mining
Subject
computer science
Application area
Pattern discovery, business process management, etc.
Process mining technology can extract information from event logs commonly generated by modern information systems. This technology provides new means for process discovery, monitoring, and improvement in various application fields. Process mining is receiving increasing attention for two reasons: on the one hand, more and more events are recorded, which can provide detailed information about the history of the process; on the other hand, it is improved and better in a competitive and rapidly changing environment The need to support business processes is growing.
Process mining is a young, cross-disciplinary, cross-disciplinary discipline, that is, the field of cross-computing intelligence and data mining and process modeling and analysis. The idea of process mining is to discover, monitor, and improve actual processes (rather than hypothetical processes) by extracting knowledge from event logs that are generally visible in modern (information) systems. Process mining includes process (automatic) discovery (that is, extracting a process model from an event log), compliance checking (that is, monitoring the occurrence of deviations by comparing models and logs), social network / organization mining, automatic generation of simulation models, models Extensions, model repairs, case predictions, and history-based recommendations.
Process mining is limited to control flow discovery. Of course, control flow discovery is the most exciting application in process mining. However, process mining is not limited to control flow discovery. It can also discover related organizational models and case models (that is, business data models). ) And time constraints.
Process mining is just a simple application of data mining. Traditional data mining techniques (such as association rules and decision trees) are not process-centric. Process mining uses a process model that supports concurrent semantics to represent mining results. Traditional data mining techniques cannot Effectively solve this problem.
Process mining is limited to offline analysis. Process mining usually extracts process knowledge from historical event data, but process mining techniques can also be applied to running cases. For example, a discovered process model can be used to predict the completion time of an executing user order.

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