Automated Discovery of Process Models from Event Logs: Review and Benchmark

Abstract

Process mining allows analysts to exploit logs of historical executions of business processes to extract insights regarding the actual performance of these processes. One of the most widely studied process mining operations is automated process discovery. An automated process discovery method takes as input an event log, and produces as output a business process model that captures the control-flow relations between tasks that are observed in or implied by the event log. Various automated process discovery methods have been proposed in the past two decades, striking different tradeoffs between scalability, accuracy and complexity of the resulting models. However, these methods have been evaluated in an ad-hoc manner, employing different datasets, experimental setups, evaluation measures and baselines, often leading to incomparable conclusions and sometimes unreproducible results due to the use of closed datasets. This article provides a systematic review and comparative evaluation of automated process discovery methods, using an open-source benchmark and covering twelve publicly-available real-life event logs, twelve proprietary real-life event logs, and nine quality metrics. The results highlight gaps and unexplored tradeoffs in the field, including the lack of scalability of some methods and a strong divergence in their performance with respect to the different quality metrics used.

Automated discovery of business process simulation models from event logs

“…We use the Split Miner algorithm [13] to generate BPMN v2.0 models from event logs. We selected this automated process discovery method because it achieves high levels of accuracy (precision and fitness) while at the same time producing simple process models, relative to other state-of-the-art process discovery techniques [14].…”

Research Challenges for Intelligent Robotic Process Automation

“…Approaches: To synthesize complex workflows through an intelligent orchestration of the robots' routines, automated planning techniques in AI can be employed (Ghallab, Nau, and Traverso 2004). The application of planning for tackling the composition issue has been already proved to be successful in real world domains (Marrella 2019). The idea is to consider the robots' routines as black boxes, i.e., as planning actions with specific preconditions and effects, and to delegate to a planning system the generation of a proper strategy to automatically compose them in a larger workflow that coordinates their orchestration.…”

Process Mining of Programmable Logic Controllers: Input/Output Event Logs

“…Split Miner [21] is a recent PN process model discovery algorithm which demonstrated major performance improvements compared to previous methods [22]. This approach has been developed to tackle the tradeoff between several process conformance measures.…”