11 Facts About Process mining

Process mining is a family of techniques relating the fields of data science and process management to support the analysis of operational processes based on event logs.

Process mining is an integral part of data science, fueled by the availability of event data and the desire to improve processes.

Process mining provides novel insights that can be used to identify the executional path taken by operational processes and address their performance and compliance problems.

Process mining uses these event data to answer a variety of process-related questions.

Process mining techniques are often used when no formal description of the process can be obtained by other approaches, or when the quality of existing documentation is questionable.

Process mining is different from mainstream machine learning, data mining, and artificial intelligence techniques.

For example, process discovery techniques in the field of process mining try to discover end-to-end process models that are able to describe sequential, choice relation, concurrent and loop behavior.

However, process mining can be used to generate machine learning, data mining, and artificial intelligence problems.

Term "Process mining" was first coined in a research proposal written by the Dutch computer scientist Wil van der Aalst.

Process mining should be viewed as a bridge between data science and process science.

Process mining focuses on transforming event log into a meaningful representation of the process which can lead to the formation of several data science and machine learning related problems.