Business process discovery

Business process discovery (BPD) related to business process management and process mining is a set of techniques that manually or automatically construct a representation of an organisations' current business processes and their major process variations. These techniques use data recorded in the existing organisational methods of work, documentations, and technology systems that run business processes within an organisation. The type of data required for process discovery is called an event log. Any record of data that contains the case id (a unique identifier that is helpful in grouping activities belonging to the same case), activity name (description of the activity taking place), and timestamp. Such a record qualifies for an event log and can be used to discover the underlying process model. The event log can contain additional information related to the process, such as the resources executing the activity, the type or nature of the events, or any other relevant details. Process discovery aims to obtain a process model that describes the event log as closely as possible. The process model acts as a graphical representation of the process (Petri nets, BPMN, activity diagrams, state diagrams, etc.). The event logs used for discovery could contain noise, irregular information, and inconsistent/incorrect timestamps. Process discovery is challenging due to such noisy event logs and because the event log contains only a part of the actual process hidden behind the system. The discovery algorithms should solely depend on a small percentage of data provided by the event logs to develop the closest possible model to the actual behaviour. Various algorithms have been developed over the years for the discovering the process model using an event log: α-algorithm - α-algorithm was the first process discovery algorithms that could adequately deal with concurrency. With an event log as the input, the α-algorithm derives various "relations" between the activities occurring in the event log.