# Special Issue on Process Mining and Emerging Applications
## 2 Special Issue  
This Special Issue consists of five articles presenting novel approaches for process mining, which significantly contribute to the advancement of our knowledge in the field and open further avenues of research.  
The first paper is entitled \"Efficient Time and Space Representation of Uncertain Event Data\" and is authored by [PERSON] et al. [1]. The work addresses the problem of analyzing event data containing uncertainty by introducing a new approach to efficiently calculate a graphical representation of the behavior contained in an uncertain process trace. The approach is studied from the theoretical viewpoint, and a number of optimizations are designed to improve its efficiency. Experiments on various uncertain event logs practically confirm its theoretical properties.  
The second paper is entitled \"Translating Workflow Nets to Process Trees: An Algorithmic Approach\" and is authored by [PERSON] and [PERSON] [2]. The authors deal with two important formalisms in process mining, namely process traces and workflow nets. In fact, while translating a process tree into a sound workflow net is trivial, the reverse is generally not possible. The work considers this task and presents an algorithm that detects whether a net corresponds to a process tree and, if so, constructs it. The algorithm is analyzed from the theoretical and the experimental viewpoints.  
The third paper is entitled \"Investigating Social Contextual Factors in Remaining-Time Predictive Process Monitoring--A Survival Analysis Approach\" and is authoredby [PERSON] et al. [3]. The work investigates the impact of social contextual features in the predictive process monitoring framework utilizing a survival analysis approach. In particular, it proposes an approach to censor an event log and to predict the remaining time of an in-flight process instance by using the survival function to estimate the throughput time for each trace, which is then used with the elapsed time to predict the remaining time for the trace. The proposed approach is benchmarked against existing approaches using five real-life event logs.  
The fourth paper is authored by [PERSON] et al. and is entitled \"Understanding Contrail Business Processes through Hierarchical Clustering: A Multi-Stage Framework\" [4]. The authors propose a multi-stage hierarchical framework for business-logic-driven clustering of highly variable process logs. According to their approach, the raw event log is initially decomposed into high-level business classes; later, feature engineering is performed exclusively based on the business-context features to support the discovery of meaningful business clusters. A case study of a CRM process of one of the UK's renowned telecommunication firms is also presented.  
Finally, the fifth paper in this Special Issue is authored by [PERSON] et al. and is entitled \"CONDA-PM--A Systematic Review and Framework for Concept Drift Analysis in Process Mining\" [5]. The work focuses on analyzing scenarios presenting a concept drift, that is, the evolution of the business process over time. Concept drift analysis is, indeed, crucial to enable early detection and management of changes, namely, whether to promote a change to become part of an improved process or to reject the change and make decisions to mitigate its effects. Within this context, the authors propose a framework describing phases and requirements of a concept drift analysis approach, and they apply it to the approaches that have been proposed in the literature.  
The Guest Editor locales no conflict of interest.