A survey on recommendation in process mining

Recommender systems have been widely applied in several domains to make informed decisions by recommending items that might be of interest. Considering recommendation during business process execution is also highly advantageous as the efficient suggestions about possible activities or resources can impact process performance. However, the deployment of the recommendation frameworks in process mining still needs more investigations to identify the current challenges to enable the practical application of research findings and ensure a large-scale adoption of this technique. Accordingly, a systematic review is conducted to provide a taxonomy of the published studies on process-aware recommender systems based on specified criteria, including the type and perspective of recommendation, a list of datasets and evaluation metrics used in the setting of PARS, implementation environments, and different algorithms used in PARS. In this regard, there are various insights extracted from this study: (i) Most studies in the business process analysis domain are of descriptive and predictive nature, (ii) recommendation in process mining is an emerging research area that is being evolving; the majority of proposals relate to 2015 and after that, and (iii) due to the lack of common evaluation protocol, datasets, and metrics, most studies are validated through experiments and prototyping, with less tendency to the practical implementation of a solution regarding real scenarios.