Motivations of Process Mining
Data are important aspects of any organization. Data can be processed to get information and information can be processed to get knowledge. Knowledge from these past data are driving forces for carrying out business in present and future. Different classical data mining techniques like clustering, classification, regression, association and sequence mining are used to analyzes specific steps in overall process but it does not focus on business process. Business process can be studied by using process mining. Some of the important motivations of process mining are summarised below:
- Explosion of event data: All the activities done by people, machines and software leaves trails called event logs. More and more events are being recorded thus providing detailed information about the history processes. As mentioned in section above, high volume of data gives higher challenges of extracting useful information from it.
- Discovering business process: It is always interesting to discover if the users of the system follows some processes. Process mining techniques take event logs and discover process within it if exits.
- Bottlenecks identifications in process: Process mining can be used to identify bottlenecks in Information Systems by analyzing the event logs data. Using right processed data, one can find bottlenecks relating to missing steps, service interruptions or long process times. Hence, process mining can be used to identify and understand
bottlenecks, inefficiencies, deviations, and risks.
- Conformance checking with existing model: For quality assurance, it is often required to check if reality as recorded in log confirms to the conceptual model. Process mining can be used for conformance checking provided conceptual model is available.
- Recommendation of news items using process mining technique: Process mining can be used to discover the process user reads news articles in general. These predicted process path can be recommended to new users visiting the site.