Detection of batch activities from event logs.

Organizations carry out a variety of business processes in order to serve their clients. Usually supported by information technology and systems, process execution data is logged in an event log. Process mining uses this event log to discover the process' control-flow, its performance, information about the resources, etc. A common assumption is that the cases are executed independently of each other. However, batch work – the collective execution of cases for specific activities – is a common phenomenon in operational processes to save costs or time. Existing research has mainly focused on discovering individual batch tasks. However, beyond this narrow setting, batch processing may consist of the execution of several linked tasks. In this work, we present a novel algorithm which can also detect parallel, sequential and concurrent batching over several connected tasks, i.e., subprocesses. The proposed algorithm is evaluated on synthetic logs generated by a business process simulator, as well as on a real-world log obtained from a hospital's digital whiteboard system. The evaluation shows that batch processing at the subprocess level can be reliably detected. • This paper presents the novel Batch Activity Mining Algorithm (BAMA). • BAMA automatically detects batching behavior in process execution data (event log). • BAMA is the first to detect batching at both the task and the subprocess level. • BAMA is empirically evaluated using both synthetic and real-world data. [ABSTRACT FROM AUTHOR]