Process mining extracts and analyzes business processes from event logs to discover new processes, compare existing ones, and improve them. There are three types: discovery, compliance, and extension. However, hidden tasks and duplicate tasks in event logs can create issues.
Process mining is a technique in which business processes are extracted from information system event logs and analyzed. It is a business process management practice employed for the purpose of discovering new processes, comparing the existing process with the workflow model, and improving the process. Data mining from event logs can yield valuable information that may not be obtainable by other methods.
There are three categories of process mining. The first is the discovery model, so named because it involves discovering previously unknown or undocumented processes. This type of data mining is performed when there is no existing model for the workflow or when the existing documentation is known to be defective. Event logs are extracted for information, which is analyzed in order to recreate the process. Documentation is created for the process, based on data extracted from event logs.
The second type of process mining is the compliance model. The name derives from its purpose of verifying that the ongoing workflow conforms to the planned process. Event logs are data extracted to find differences between the existing process and the model.
Once these differences are located, they are analyzed to see if they have improved the process. If such changes are beneficial to the process, the model will be revised to include these deviations. Decisions made at process checkpoints are reviewed for the information available at each point and the data that affect those decisions. If such changes prove to be disadvantageous, changes can be made to the existing process to allow it to adapt more quickly to the model.
The third class of process mining is the extension model. This type of data mining seeks to extend an existing model with an improvement. Data from event logs is analyzed for possible areas of improvement in model structure. Bottlenecks, for example, can be checked for possible alternative routes in the workflow.
Process mining is not without difficulties. Some tasks are invariably hidden from event logs and cannot be extracted from data. These can be reconstructed through careful analysis of visible tasks, but not always. Conclusions based solely on information drawn from event logs may therefore be of questionable quality.
Duplicate tasks in the event log also create problems as there may be different activities under the same category or task name. Therefore, it can be difficult to distinguish tasks with the same name despite having different functions. Other issues include adequate data on decision making, incorporating time into the model, differing perspectives, incorrectly recorded data, and simply not enough information. Process mining must be tempered with experience and common sense to overcome these problems when applying this technique.
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