Process mining is a combination of techniques relating to the fields of data science and process management to support the analysis of operational processes based on event logs. The goal of process mining is to turn event data into insights and actions. Process mining techniques use event data to show what people, machines, and organizations are really doing. Process mining provides novel insights that can be used to identify the execution path taken by operational processes and address their performance and compliance problems.
Process mining starts from event data. Input for process mining is an event log. An event log views a process from a particular angle. Each event in the log should contain
A unique identifier for a particular process instance (called case id)
An activity (description of the event that is occurring)
A timestamp.
With some effort, such data can be extracted from any information system supporting operational processes. Process mining uses these event data to answer a variety of process-related questions.
There are three main classes of process mining techniques
Process Discovery:
The main goal of process discovery is to transform the event log into a process model. An event log can come from any data storage system that records the activities in an organization along with the timestamps for those activities. Such an event log is required to contain a case id (a unique identifier to recognize the case to which activity belongs), activity description (a textual description of the activity executed), and timestamp of the activity execution. The result of process discovery is generally a process model which is representative of the event log.
Conformance checking:
Helps in comparing an event log with an existing process model to analyze the discrepancies between them. Such a process model can be constructed manually or with the help of a discovery algorithm. For example, a process model may indicate that purchase orders of more than 1 million euros require two checks. Another example is the checking of the so-called "four-eyes" principle. Conformance checking may be used to detect deviations (compliance checking), or evaluate the discovery algorithms, or enrich an existing process model.
Process Enhancement:
The process model is extended with additional performance information such as processing times, cycle times, waiting times, costs, etc., so that the goal is not to check conformance, but rather to improve the performance of the existing model with respect to certain process performance measures.
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